{
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  {
   "cell_type": "code",
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   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
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    }
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/daily-climate-time-series-data/DailyDelhiClimateTrain.csv\n",
      "/kaggle/input/daily-climate-time-series-data/DailyDelhiClimateTest.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "from subprocess import check_output\n",
    "from keras.layers.core import Dense, Activation, Dropout\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.models import Sequential\n",
    "from sklearn.model_selection import  train_test_split\n",
    "import time #helper libraries\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import newaxis\n",
    "import seaborn as sns\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "# for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "#     for filename in filenames:\n",
    "#         print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:45:08.966743Z",
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     "shell.execute_reply": "2024-07-02T15:45:09.009577Z",
     "shell.execute_reply.started": "2024-07-02T15:45:08.966663Z"
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   "outputs": [
    {
     "data": {
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       "      <th>date</th>\n",
       "      <th>meantemp</th>\n",
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       "      <th>meanpressure</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>84.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1015.666667</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>2.980000</td>\n",
       "      <td>1017.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>7.166667</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>4.633333</td>\n",
       "      <td>1018.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>8.666667</td>\n",
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       "      <td>1.233333</td>\n",
       "      <td>1017.166667</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>86.833333</td>\n",
       "      <td>3.700000</td>\n",
       "      <td>1016.500000</td>\n",
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      ],
      "text/plain": [
       "         date   meantemp   humidity  wind_speed  meanpressure\n",
       "0  2013-01-01  10.000000  84.500000    0.000000   1015.666667\n",
       "1  2013-01-02   7.400000  92.000000    2.980000   1017.800000\n",
       "2  2013-01-03   7.166667  87.000000    4.633333   1018.666667\n",
       "3  2013-01-04   8.666667  71.333333    1.233333   1017.166667\n",
       "4  2013-01-05   6.000000  86.833333    3.700000   1016.500000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('./DailyDelhiClimateTrain.csv')\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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    {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>meantemp</th>\n",
       "      <th>humidity</th>\n",
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       "      <th>meanpressure</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>15.913043</td>\n",
       "      <td>85.869565</td>\n",
       "      <td>2.743478</td>\n",
       "      <td>59.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>77.222222</td>\n",
       "      <td>2.894444</td>\n",
       "      <td>1018.277778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>17.111111</td>\n",
       "      <td>81.888889</td>\n",
       "      <td>4.016667</td>\n",
       "      <td>1018.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-01-04</td>\n",
       "      <td>18.700000</td>\n",
       "      <td>70.050000</td>\n",
       "      <td>4.545000</td>\n",
       "      <td>1015.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-01-05</td>\n",
       "      <td>18.388889</td>\n",
       "      <td>74.944444</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>1014.333333</td>\n",
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       "         date   meantemp   humidity  wind_speed  meanpressure\n",
       "0  2017-01-01  15.913043  85.869565    2.743478     59.000000\n",
       "1  2017-01-02  18.500000  77.222222    2.894444   1018.277778\n",
       "2  2017-01-03  17.111111  81.888889    4.016667   1018.333333\n",
       "3  2017-01-04  18.700000  70.050000    4.545000   1015.700000\n",
       "4  2017-01-05  18.388889  74.944444    3.300000   1014.333333"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('../input/daily-climate-time-series-data/DailyDelhiClimateTest.csv')\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "shell.execute_reply.started": "2024-07-02T15:45:09.037005Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "date            0\n",
       "meantemp        0\n",
       "humidity        0\n",
       "wind_speed      0\n",
       "meanpressure    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:45:09.052483Z",
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     "shell.execute_reply": "2024-07-02T15:45:09.061323Z",
     "shell.execute_reply.started": "2024-07-02T15:45:09.052447Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date            0\n",
       "meantemp        0\n",
       "humidity        0\n",
       "wind_speed      0\n",
       "meanpressure    0\n",
       "dtype: int64"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "test.isna().sum()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:45:09.067352Z",
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   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meantemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>meanpressure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1462.000000</td>\n",
       "      <td>1462.000000</td>\n",
       "      <td>1462.000000</td>\n",
       "      <td>1462.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25.495521</td>\n",
       "      <td>60.771702</td>\n",
       "      <td>6.802209</td>\n",
       "      <td>1011.104548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.348103</td>\n",
       "      <td>16.769652</td>\n",
       "      <td>4.561602</td>\n",
       "      <td>180.231668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.428571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18.857143</td>\n",
       "      <td>50.375000</td>\n",
       "      <td>3.475000</td>\n",
       "      <td>1001.580357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.714286</td>\n",
       "      <td>62.625000</td>\n",
       "      <td>6.221667</td>\n",
       "      <td>1008.563492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31.305804</td>\n",
       "      <td>72.218750</td>\n",
       "      <td>9.238235</td>\n",
       "      <td>1014.944901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.714286</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>42.220000</td>\n",
       "      <td>7679.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          meantemp     humidity   wind_speed  meanpressure\n",
       "count  1462.000000  1462.000000  1462.000000   1462.000000\n",
       "mean     25.495521    60.771702     6.802209   1011.104548\n",
       "std       7.348103    16.769652     4.561602    180.231668\n",
       "min       6.000000    13.428571     0.000000     -3.041667\n",
       "25%      18.857143    50.375000     3.475000   1001.580357\n",
       "50%      27.714286    62.625000     6.221667   1008.563492\n",
       "75%      31.305804    72.218750     9.238235   1014.944901\n",
       "max      38.714286   100.000000    42.220000   7679.333333"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:45:09.101515Z",
     "iopub.status.busy": "2024-07-02T15:45:09.101100Z",
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     "shell.execute_reply.started": "2024-07-02T15:45:09.101479Z"
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    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>meantemp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_speed</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>114.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>114.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.713079</td>\n",
       "      <td>56.258362</td>\n",
       "      <td>8.143924</td>\n",
       "      <td>1004.035090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.360072</td>\n",
       "      <td>19.068083</td>\n",
       "      <td>3.588049</td>\n",
       "      <td>89.474692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>17.750000</td>\n",
       "      <td>1.387500</td>\n",
       "      <td>59.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.437198</td>\n",
       "      <td>39.625000</td>\n",
       "      <td>5.563542</td>\n",
       "      <td>1007.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>19.875000</td>\n",
       "      <td>57.750000</td>\n",
       "      <td>8.069444</td>\n",
       "      <td>1012.739316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27.705357</td>\n",
       "      <td>71.902778</td>\n",
       "      <td>10.068750</td>\n",
       "      <td>1016.739583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>34.500000</td>\n",
       "      <td>95.833333</td>\n",
       "      <td>19.314286</td>\n",
       "      <td>1022.809524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         meantemp    humidity  wind_speed  meanpressure\n",
       "count  114.000000  114.000000  114.000000    114.000000\n",
       "mean    21.713079   56.258362    8.143924   1004.035090\n",
       "std      6.360072   19.068083    3.588049     89.474692\n",
       "min     11.000000   17.750000    1.387500     59.000000\n",
       "25%     16.437198   39.625000    5.563542   1007.437500\n",
       "50%     19.875000   57.750000    8.069444   1012.739316\n",
       "75%     27.705357   71.902778   10.068750   1016.739583\n",
       "max     34.500000   95.833333   19.314286   1022.809524"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:45:09.133573Z",
     "iopub.status.busy": "2024-07-02T15:45:09.133094Z",
     "iopub.status.idle": "2024-07-02T15:45:09.147947Z",
     "shell.execute_reply": "2024-07-02T15:45:09.146570Z",
     "shell.execute_reply.started": "2024-07-02T15:45:09.133516Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             object\n",
       "meantemp        float64\n",
       "humidity        float64\n",
       "wind_speed      float64\n",
       "meanpressure    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:45:09.150092Z",
     "iopub.status.busy": "2024-07-02T15:45:09.149639Z",
     "iopub.status.idle": "2024-07-02T15:45:09.163536Z",
     "shell.execute_reply": "2024-07-02T15:45:09.162494Z",
     "shell.execute_reply.started": "2024-07-02T15:45:09.150054Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date             object\n",
       "meantemp        float64\n",
       "humidity        float64\n",
       "wind_speed      float64\n",
       "meanpressure    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:45:09.165349Z",
     "iopub.status.busy": "2024-07-02T15:45:09.164977Z",
     "iopub.status.idle": "2024-07-02T15:45:09.176195Z",
     "shell.execute_reply": "2024-07-02T15:45:09.174835Z",
     "shell.execute_reply.started": "2024-07-02T15:45:09.165314Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1462, 5)\n",
      "(114, 5)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:45:09.178240Z",
     "iopub.status.busy": "2024-07-02T15:45:09.177816Z",
     "iopub.status.idle": "2024-07-02T15:45:09.378980Z",
     "shell.execute_reply": "2024-07-02T15:45:09.377984Z",
     "shell.execute_reply.started": "2024-07-02T15:45:09.178201Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7a0cd720a050>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=train['meantemp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:45:09.381274Z",
     "iopub.status.busy": "2024-07-02T15:45:09.380789Z",
     "iopub.status.idle": "2024-07-02T15:45:09.516872Z",
     "shell.execute_reply": "2024-07-02T15:45:09.515846Z",
     "shell.execute_reply.started": "2024-07-02T15:45:09.381236Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7a0cd7016690>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAALfUlEQVR4nO3dcaydd13H8c93t+o6EHUdztmhHeviFBJBGwOoyQKbVjMmUQzTkE2MITHalSkYp1GjiX9JjFtVkjFlbUAIAcQxZ92GmGhQTCtLNtkWrmyDVdhKF8diB3rbn3+cU7mZ99Leu51+e+99vZKm5zzPc8/z6y/nvvvc55zz3BpjBIDT76zuAQBsVAIM0ESAAZoIMEATAQZosmklG5933nlj27ZtMxoKwPp08ODBL44xXvjM5SsK8LZt23LgwIHnblQAG0BVPbLUcqcgAJoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaLKi3wkHz8aePXsyPz/fPYxTcujQoSTJ1q1bm0eSbN++Pbt27eoeBjMgwJw28/Pzuee++3PsnHO7h3JSc0efTJJ84Su93yJzR59o3T+zJcCcVsfOOTdPX/rj3cM4qc0P3JEk7WM9MQ7WJ+eAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGarKkA79mzJ3v27OkeBrCBzLI7m2byqDMyPz/fPQRgg5lld9bUETDAeiLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQ5LQE+MiRI7nuuuty5MiRVa0HWI9OS4D37t2be++9N/v27VvVeoD1aOYBPnLkSPbv358xRvbv3///jnJPth5gvdo06x3s3bs3x48fT5IcO3Ys+/bty/XXX3/K6xc7dOhQnn766ezevXvWw2YG5ufnc9Z/j+5hrClnfflLmZ9/ynO+0fz8fDZv3jyTxz7pEXBVvbmqDlTVgcOHD694B3fffXcWFhaSJAsLC7nrrrtWtB5gvTrpEfAY4+YkNyfJjh07Vnz4cvnll+eOO+7IwsJCNm3alCuuuGJF6xfbunVrkuTGG29c6TA4A+zevTsHP/NY9zDWlONnvyDbX3y+53yjWf70MfNzwNdee23OOmuym7m5uVxzzTUrWg+wXs08wFu2bMnOnTtTVdm5c2e2bNmyovUA69XMX4RLJke5Dz/88LJHtydbD7AenZYAb9myJTfddNOq1wOsRz6KDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoMmm7gGsxPbt27uHAGwws+zOmgrwrl27uocAbDCz7I5TEABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQBMBBmgiwABNBBigiQADNBFggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZosql7AGwsc0efyOYH7ugexknNHT2SJO1jnTv6RJLzW8fA7Agwp8327du7h3DKDh1aSJJs3dodv/PX1LyxMgLMabNr167uIcAZxTlggCYCDNBEgAGaCDBAEwEGaCLAAE0EGKCJAAM0EWCAJgIM0ESAAZoIMEATAQZoIsAATQQYoIkAAzQRYIAmAgzQRIABmggwQJMaY5z6xlWHkzwyu+Gs2nlJvtg9iDOQeVmeuVmaeVnas52X7xxjvPCZC1cU4DNVVR0YY+zoHseZxrwsz9wszbwsbVbz4hQEQBMBBmiyXgJ8c/cAzlDmZXnmZmnmZWkzmZd1cQ4YYC1aL0fAAGuOAAM0WVMBrqoXVdXHqur+qvq3qto9XX5uVd1VVZ+e/v0t3WPtUFVzVfXJqrp9et+8JKmqb66qD1TVA9PnzivNTVJV10+/j+6rqvdW1dkbdV6q6s+r6vGqum/RsmXnoqpuqKr5qnqwqn50tftdUwFOspDkV8cY353kFUl+qaq+J8mvJ/noGOOSJB+d3t+Idie5f9F98zJxY5L9Y4xLk3xvJnO0oeemqrYmuS7JjjHGS5PMJbk6G3debk2y8xnLlpyLaXOuTvKS6df8aVXNrWqvY4w1+yfJXyW5IsmDSS6YLrsgyYPdY2uYiwunT5JXJ7l9usy8JC9I8lCmLzgvWr6h5ybJ1iSfS3Jukk1Jbk/yIxt5XpJsS3LfyZ4jSW5IcsOi7f42yStXs8+1dgT8f6pqW5KXJ/lEkvPHGJ9Pkunf39o3sjZ/lOTXkhxftMy8JC9OcjjJu6anZ26pqudlg8/NGONQkrcn+WySzyd5coxxZzb4vDzDcnNx4j+vEx6dLluxNRngqnp+kg8mecsY40vd4+lWVVcmeXyMcbB7LGegTUm+L8k7xhgvT/Jf2Tg/Vi9rej7zJ5JclOTbkzyvqt7YO6o1o5ZYtqr38665AFfV12US3/eMMT40XfxYVV0wXX9Bkse7xtfkB5NcVVUPJ3lfkldX1btjXpLJ0cmjY4xPTO9/IJMgb/S5uTzJQ2OMw2OM/0nyoSSvinlZbLm5eDTJixZtd2GS/1jNDtZUgKuqkvxZkvvHGH+4aNVtSa6d3r42k3PDG8YY44YxxoVjjG2ZvDjwd2OMN2aDz0uSjDG+kORzVfVd00WvSfKpmJvPJnlFVZ0z/b56TSYvTm70eVlsubm4LcnVVfUNVXVRkkuS/MtqdrCmPglXVT+U5B+S3Juvnuv8jUzOA78/yXdk8sT66THGEy2DbFZVlyV56xjjyqraEvOSqnpZkluSfH2SzyR5UyYHHxt6bqrqd5O8IZN3F30yyS8keX424LxU1XuTXJbJZScfS/I7ST6cZeaiqn4zyc9nMndvGWP8zar2u5YCDLCerKlTEADriQADNBFggCYCDNBEgAGaCDCnXVVtW3zVqefwcX+vqi5fYvlli64Qd1VVnbioyuumF1aBFpu6BwDPlTHGb5/CNrdl8kb6JHldJheh+dQsxwXLcQRMl7mqeuf0erR3VtXmqvr7qtqRJFV13vSj1amqn6uqD1fVR6rqoar65ar6lenFdf65qs6dbndrVb1+envn9Pq//5jkJ0/sdPpYf1xVr0pyVZI/qKp7quriqvrXRdtdUlWurcFMCTBdLknyJ2OMlyT5zyQ/dZLtX5rkZ5P8QJLfT3J0enGdf0pyzeINq+rsJO9M8tokP5zk2575YGOMj2dyJPy2McbLxhj/nuTJ6afmksmn5W5d3T8NTo0A0+WhMcY909sHM7kW69fysTHGU2OMw0meTPKR6fJ7l/jaS6eP/+kx+ajnu09xTLckedP04tpvSPIXp/h1sCoCTJevLLp9LJPXIxby1efk2V9j++OL7h/P0q9lrOYz9h9M8mNJrkxycIxxZBWPAadMgDmTPJzk+6e3X/8sHueBJBdV1cXT+z+zzHZPJfnGE3fGGF/O5LcbvCPJu57F/uGUCDBnkrcn+cWq+ngmV6ValWlI35zkr6cvwj2yzKbvS/K26Yt5J2L9nkyOnu9c7f7hVLkaGixSVW9N8k1jjN/qHgvrn/cBw1RV/WWSizP5xaYwc46AAZo4BwzQRIABmggwQBMBBmgiwABN/hfn13diUo0HKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=train['humidity'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:45:09.518858Z",
     "iopub.status.busy": "2024-07-02T15:45:09.518383Z",
     "iopub.status.idle": "2024-07-02T15:45:09.651339Z",
     "shell.execute_reply": "2024-07-02T15:45:09.650265Z",
     "shell.execute_reply.started": "2024-07-02T15:45:09.518807Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7a0cd7010690>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hkscCLAcBRloeUOFL+EPufkrSH7X48kM175N0d/JNuNkl9n2XpIeTSwfXSBpPtk9J6jezhyRtUuE67jOS3iHpM2b2C0lHVLj0IBXi+v5k+yO6+PsLB1X45aAPSnruMt4DUBW3o8RlKfl9ct9xd35VD5oWZ8AAEIQzYKwZZvY3kj6zYPNj7v72iOMBVosAA0AQLkEAQBACDABBCDAABCHAABDkz3W6qoS7DVnhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=train['wind_speed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:45:09.653492Z",
     "iopub.status.busy": "2024-07-02T15:45:09.653027Z",
     "iopub.status.idle": "2024-07-02T15:45:09.802738Z",
     "shell.execute_reply": "2024-07-02T15:45:09.801666Z",
     "shell.execute_reply.started": "2024-07-02T15:45:09.653438Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7a0cd6f76910>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pt+4686Vl+RskPR8Ro5J+T9XZ6GRcX/f+++N/Mar7ej9n+7pyPNt+Z3n81oh4PCLulPSipHNtny/pvyPic6rudvYOVV9A3mR7ke0OHb+8Mt4eSUtsX1n2P9/2xZP8OIBJI9Rotc+o+rmLT5Zv0H2mLP+CpHW2/1PVZY9DE2w/Xkc5A14v6ZMTrPO7kj5mu3YHu2vL8r909QNFd0n6rqo73F0vaVe5xPI2SfdEda/hP1X1U3ceVHW3tBNExP9J+m1V38DcoeouiFdN8uMAJo275+E1w9UPC+iOiBfnehZgNnFGDQDJcUYNAMlxRg0AyRFqAEiOUANAcoQaAJIj1ACQ3P8DpPHuCWUmqXwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=train['meanpressure'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:45:09.804887Z",
     "iopub.status.busy": "2024-07-02T15:45:09.804403Z",
     "iopub.status.idle": "2024-07-02T15:46:05.981235Z",
     "shell.execute_reply": "2024-07-02T15:46:05.980179Z",
     "shell.execute_reply.started": "2024-07-02T15:45:09.804848Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'MeanPressure')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 7200x5760 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(100,80))\n",
    "fig, axs = plt.subplots(2, 2,figsize=(20,20))\n",
    "axs[0, 0].plot(train['date'],train['meantemp'])\n",
    "axs[0, 0].set_title('Mean temp')\n",
    "axs[0, 1].plot(train['date'],train['humidity'], 'tab:orange')\n",
    "axs[0, 1].set_title('Humidity')\n",
    "axs[1, 0].plot(train['date'],train['wind_speed'], 'tab:green')\n",
    "axs[1, 0].set_title('Wind Speed')\n",
    "axs[1, 1].plot(train['date'],train['meanpressure'], 'tab:red')\n",
    "axs[1, 1].set_title('MeanPressure')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:46:05.983510Z",
     "iopub.status.busy": "2024-07-02T15:46:05.983132Z",
     "iopub.status.idle": "2024-07-02T15:46:19.707677Z",
     "shell.execute_reply": "2024-07-02T15:46:19.706373Z",
     "shell.execute_reply.started": "2024-07-02T15:46:05.983473Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a scatter plot\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.scatter(train['date'], train['meantemp'])\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Mean Temperature')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:46:19.710026Z",
     "iopub.status.busy": "2024-07-02T15:46:19.709505Z",
     "iopub.status.idle": "2024-07-02T15:46:33.575063Z",
     "shell.execute_reply": "2024-07-02T15:46:33.574080Z",
     "shell.execute_reply.started": "2024-07-02T15:46:19.709972Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a scatter plot\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.scatter(train['date'], train['humidity'])\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Humidity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:46:33.576833Z",
     "iopub.status.busy": "2024-07-02T15:46:33.576451Z",
     "iopub.status.idle": "2024-07-02T15:46:47.349410Z",
     "shell.execute_reply": "2024-07-02T15:46:47.348258Z",
     "shell.execute_reply.started": "2024-07-02T15:46:33.576797Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GNX51soKTh28fQIuKzQcOvwDTiiEA/ODYnf1uDiGEFBIhxGkp5bTveyEZ8wkZWejQGofNx4S+J4QQEk9uQpgQ4iYhxMtCiO8KIc4JIf6w9fkWIcRfCCG+1/pzc15tIMQHhYo46HtCCCHZkacm7DqAT0sp/3MAewH810KIXwJwGMDXpJS/COBrrb8TEkTWTvQUKuKg78now0AVQvqHK1nr3a4fSimf9fz72wDebv33T4QQ3wVQRbP25K+3vvZ5AF8H8EfBLSZjSx5ZofuV0XqU6EfySTIYmHmdkP7iyvd1V+vP9wP4NQAvtf6+H03BySmE6QghtgOYAvAtAD/XEtAgpXxbCPH+qBaTsSWvrNAUKghpwszrg4X1H8cPqxAmpfwnACCE+CqAX1KCkxDiBgD/OvQGQoj3AHgGwINSyh8LIUJ/90kAnwSAbdu2hd6OjDB0ou+GizbJEs6xwUEt5HgSkvl+uxLAWvwtgL8TcnEhRBlNAeyLmvnyb4UQN7S0YDcA+KHpt1LKzwH4HNBMURFyPzLaMCt0J1y0SdZwjvUX/RA1IQSWEymjqIXsjWE4pIY45n9dCHFCCPGPhRCfAPACgJd9PxJNldf/CeC7Uso/1v7pOTRrUaL151ci20zGFDrRd5Jn4lQynnCO9Y9kouikAKagFjIdw5KI26sJk1L+Ny0n/f+i9dHnpJR/FnDtfQD+EYCzQojF1mf/HYBjAJ4SQvw+gDcAfDy+2WQcoRN9JzQdkazhHOsfpkOUCWoh0zEs/o1BhbhbpsRgR/zWb/4/NBNpm/iNmGsRoqAT/So0HZE84BzrDyGHJWoh0zMsh1SvOVIIcXcrseplIcSPhRA/EUL8uB+NI4TYoemImCh6nq+it69f2A5LJSGYgy8DhiURd4gm7F8CuEtK+d28G0MICYemI5Kk6MEaRW9fP5k9sAOzT59BY2XVF6w8IXD847vH7l3kweyBHR1jDSjmITVECPtbCmDExzBEoYwigzAdsa+LS9H9YIrevr6TdNgJy+BEAhiWQ2qIEHZKCDEHYB7Au+pDX8Z8Mj7wdDs+sK+LTdH9YIrevn5y/MR5NJY7IyIby7JLIOWhJz3D4N8YkqLivQCuArgDzSz6dwH4SJ6NIsMFUyWMD+zrYmPzd9lUKfe5JWaGxU+nH4QIpMOSZoGkxyuESSn/ieF/v9ePxpHhgKfb0cTkQM2+LjazB3YYF/Ur164XYuNmMMkqIQIpDz2jj6uA9z+TUv5LIcS/AtCVRU5K+Qe5towMDUyVMHrYzI6TG8q4dLXR9X32dXFYMXxmMnMNgmHx0+kHIY7jw3zooRk1DJdPmHLGP9WPhpDhZViiUMaZ2AXRdgJft2YClXIpqK+5CPcfl4akKBv3MPjp9IMQgXRYD7j0HQ3HJYR9UAjxq2jWfbzerwYNO4PYeAa92fF0W2zSLIi2DftyvYEnDu7x9jUXYTe+OZt2TrsEraJv3OOITyAd1gMuo2DDcQlhPw/gfwawUwjxHQDfAHASwDellBf70bhhw7fx5CEsFWWz4+m2uKRZEF0n8JC+5iJsJ2SdSDunbf0mgMJv3KSbYT3gDrMZtd9YHfOllP+tlPLXAPxnaNZ8vAjg9wD8ByHEf+xT+4YK18aTV5QLHTeJjzQLYq8O1FyE7fjmbC9z2tRvAsD9e7cVfuMmZmamqjh5+Hb84NidOHn49qHoR0bBhhOSoqKCZpqKTa3/vQXgW3k2alhxbTx5CUvc7IiPNAvizFQVj9+9C9XJSqoSKlyE7fjmbC9z2tRvTxzcg8dmdqVuLyGxMAo2HFd05OcA3ALgJ2gKXd8A8MdSykt9atvQ4TLh5CUsFclxc9C+acRMWr+SXkzMw+rL0g98czZmTtvmXEy/cd7Gw3fmZljNqIPA5RO2DcA6AN8DUAPw1wCW+tGoYcW18Rw/cT4XYakom11RfNNM7Rr3hWAQCyIXYTu+ORs6p3uZc2pe1JbqEFjNQVRbqmP26TM4+vw5LF1tsN8MFHWtKxr0Ew5DSNmVAmz1H4UQaGrDfq31v19G0zfsm1LKh/vSQgDT09Py1KnhyJRh2/STExdoLqwxJp7Ye/aTfcdeMgqZ1ckKTh6+va9tUeT5zgnphSyiI9POOdO8cME500kR1zpSPIQQp6WU077vOWtHyqaE9h+EEEsALrf+9xEAHwbQNyFsmLBJ/3lqBopw4iiibxoj9IohoJNufHM2ZE6nnXOmeeFi3OaMjyKudWR4cfmE/QGa2q99ABpopacA8KcAzvaldSNGEYSlvCiSb5pi3BdLmk1Gm7RzLs34H5c5E0IR1zoyvLiiI7cD+DKAD0spf0FK+Y+klP9GSnlGSmmqjEEKiKn+Xx4UMRpmXCL0bH3M9CWjTdo5l2b8j9qc6YUirnVkeHHlCfuUlPLLUsq3+9kgkh155SYz0WtKgzwYh8XS1McPzi1i6tEXjad1gFqNUSHtnLPlEgOAyUoZ5ZLo+LdRmzO9UsS1jgwvTsf8ojBMjvlFgg6ko+8TZetjAB1Rbzrj1P/EjGtejPqcKRJ816NLJo75JB/6NfHG3ScKGG0/PMDdlxLdgpgAsH/n1pxbRYqOa16M+pwpCvTZzJdhEXAphPWZrCeea6DRgXT0sfWxIimISQDPnK5h+uYthVyQSJNh2UBIemw+mw/OLeL4ifMj3ed5j+9hEnBDyhaRSFzO8Fk6S/t8vsbBJ6rI9CMowtTHOiUhukySdM4vNv305SSDw6XFHuU+78f4HqagJAphGeMbYLaJV1uqR2/WvoFGB9LBEbvQpBXYVB9PVspd/1Ypl7Bs8fnMwyTdr0jcUWeYNhCSHp9FYlT7vB/je5hccWiOzBhfglCX+UjfrAG/2jRkoA3Cv4OmlLhEsb2qzlUfm957XuWykgyT+r/o+A5q4zqnRg1TeaokPqFhGNfafghIw+SKQyEsY3wDLGTihWaoLuJA42bcJGahySqzv03g7kdtUVYnyI4sD2r9YhiFgV7p9Zn1Kiq2/nat5Q/Nn8UXX3mjo+5n0caFiX7sW0WpqRwCzZEZ40sQmjQR2gg5FRTR52vUTSmhJreYRLF5ngz7ZZJ2aW+KSlHNpz4/P6BYc2ocfdiyeuaZqSpOHr4dTx7cE7WWzy/UOgQwRZHGhY2s9i3X/B0mVxxqwjImRALXNRa2PE8hp4I861GmZZhs8bHEaPliTmJ5nwz7YZK2PYNA870VbfErssZ2ZqqKU69fNG6yOkWZU+OoBc36mWPX8qPPn7OOjaKMCxtZ7Fsh83dYUq1QCMuY2AGWRm1aZNV/EU2kWRGz8MaMg7xU5/0cJ7MHduDQ3GLXxiCBQm7GWW2i6h3XluooCYFlKVF1vOvQPnn51QtOAQwAJoQohI/YKB+8bOTxzKFCw/xCDZeuNqz/Pgxrba8C0igJ/hTCciBmgMUKbUU+wQPNRKBfeOUN4+fDTuzCGzoO8tBo9nuczExV8eDcovHfirgZZ7GJJt+xikS1veuYPglph36/2afPGK+TF7owOdESPpMMgzCQlkEeNl3mRgHk6o5SFAXAKAn+FMIKQIzQVvQTwMuvXjB+/tUzb+OxmV19bk225LnwZq06z3ucmBbj6hBpQbPoS9M7VpjedUyfuJzzTeWoGisSh55axKG5xdw3R5vwqePzZyrCRt4Lg3T8dgka9+/d1rd+H6QCYJQsLnTMHzKKfgKwtWOp3hh6R91+BEJk5Sye5zixOSXv37m1cIEiNrLoS9+7TP57TJ/Y2vfkwT1WM6WUyDUnncImfJaE8DpBj4oT/yAdv22ChhDA9M1bcrtvkYKubJaVYbS4UBM2ZIScAPp10jTdx3WCL4q2Li15B0JkedLM86RoW4xffvUCHr9711BoObLoS1/JqOS7jukTV/tsZl+dvHLSAXZhckVK/ODYnc7fFl2TH8OgHL9taY6kRK6aqSIpAGwWF9vnRYZC2JDhU4P3S2Vsu889t1aNPmFAcbR1vZDnwpvlBpWnucS1GA9LRBLQe1+6cv6Z3nVsn9jat3lD2emYrUj20/xCDZ9+6kyX+TB2jPUi4BdpIx9WVD9l0ZcxFMkEOErjiObIAmMyG/jU4P1SGbu0IZs3dJfQAYbTXp8Xpr6NXVgGlScnJgfaKKO/Y6BpjgPs7zqrPnn4rltQLrmyDDZJasePPHvWWsYqJp9bL6Zcjh07MWbimakqVvpYkgzw93s/8+6N0jiiJqyg+DRatoU7dCPv1WTpus8TB/cMTbbiQWDr20mLhsO0sAwyT84wZaPOm9h3nEWfJE2VmyplXLl2HY3l1U052R+uIAIgLp9bL6Zcjh0zaSwY/dZMufq93077ozSOKIQVlLSmqVCfsV4njOs+RUwiWyRsfbtuzQQq5VLQwpKl6TJWIGf/Dp6kMOfrQ592JDafW6wwqbdvU6WM9eUJLF1tcOy0SDOfsxREQtcAW7/329dvlNYgCmEFxVUGxnRi1ZNGJkPYQ07FuslykAlGxwFb316uN/DEwT1B7z8rn4i0Avkw+X6NA77+8AURAL2ZsVybeHKMLdUbqJRLeOLgHo6hFmnmc1aCSJ7BGnn6aI3KGkQhrKC4Fs3kBElOIlc9sZmpqlPAC52MRVJN94usok59WsSQa2ZliujnCXYU8kMNK64gAkXaDPy++T5KEZF5kXY+ZyGIZNE/WaxH47o+UAhLUJSB4Fo0kxPE5+8BNBfGQ3OLeHBuEcKU7RFNx+KYyVgU1XQ/yFKwnD2wA7NPn0FjZbUTyhMiSosYEiVrE5D1z22CftYn2FEVzH0k3/f+nVvx8qsX+r6+6GuFSVsO+DP+2/DN91GKZMuLIiZ/jemfDWvNMX6hebvGdX0AGB3ZQZESCapIKhv6BAmdLGrRNQXVlEvm0iMx1/d9f5gX3cyjTpPBbf5gtw5ckXa2cfzQ/Nmuz223zdq5t0iJHvuFqR++8MobA1tfZqaqOHn4drx27E48cXBPe+yoqE6dmL7xzfdRimTLizyjmX302j8PzZ/F9354xfhvoXm7xnF9UFATppGFBidEkxbjBKlOrknUBJlfqFlrt8VwfcX++9jFskj5ZLIiS8Hy+InzHZFsANBYlvj0U3H1/2I1kV/61ptd40SiuwxOHifwURTMfYRoqE3rS9baeNv11DU/cPgF4+9C+8Y33+k/GsagfJx61cx/6VtvWv/NN4Z0X+Y0vx8FqAnT6HWjCNGkxWrbXOUZfLl/YrBdIs1i2Y/yPv0my9O8bTwtS5mJZsR1fRMSyP0EPo7akNB1Q/9e1tr4kOv12je++T5ILc84kipfVw+aedf+4xpD+th0/b6f+ccGATVhGr1qcEI0abHaNldB7JdfveA9afdCNeUpfJTChxWm03x5QuDqtevRzswuX6x6YxlHnv1OT+/Kdv2SRWNanazg5OHbU98vhHHUhoREJKrvKWLWB19Eok3DkLxer30zM1XFqdcvtjWtJSFwz62dWp1RiWTLkjz8j9P4Vtk086EaWtu6AsA5hnya4kq5hP07t468rxiFMI39O7fii6+8kdo0E6JJi9W2uQpiL9XtpUuqrQ1ATRCLL74VAfS0MY/aomtLkKmSq8YsDr5ItXpjBff/yTfx2o/qqRZo26Z6z61VPHO6NhBBaBQFcx8hEYnJ9++KXNaZX6hh9stn2ptnbamOB1uBN5OG5K1J9Pv02jfzCzU8c7rW3oiXpcQzp2uYvnnLSPdvL+TliJ7GpSZk73G1977bbjKWqtv3QXf/uzTFSgGQRTqlokMhrIVaSPRlSwBdJzoXIZq0WG2b6zQdo9lIJksUAli62rD6k42ymSgtumC579hLXUJwqP+g+ndT7TfFye9fbP937ALt2lSnb94ysIVr1ARzH6Z+8EVH2uZ7MqP90efPWYUs1+FMv0+yrWn7ZhSjofMmr3eWxqUmZE9ytVftNbom9L7bbsJjM/bAMtd9S0LgraW601csJp1S0aEQ1sI0yCTCojtiEqXGqv5nD+zAg3OLxn9bltKYYX3/zq3Yd+wlqyNusu3jZiZKiy7I2nQMoX5Aqi9sfZskdoG29fe4CUKDwHTgUUzfvMW5Oc0e2IFDc4td40uiKbQfmlvEjZOVoALeNrKe3+MYdBGKzYSX1ztL41ITsif52vvYzPWPsC8AACAASURBVC6v0BVyX6AzVYrNghObTqnI0DG/RdpJkXQuVNFmwKoDKoC2Y+HxE+dxz63VYCfVmamqtSC2+q1+LWVyCnXs1Z1mgdXBffzE+ZFzgOyFpIOzjRgNYuxikSYbflqH1lF3hg0l9j0kx8lSvYFLVxvBTvYzU1Xr+FqWsn2dtOThFD+OQRchmIIiZp8+g6lHX7T2ca/vLE1QVEjgRB59nLyvKVWKvp8qKuVSZumUigA1YS3SOuXbNGjKJGiypT9zutYxyNVCbzNRPHzXLdaTSlKzse/YS9EnBPX5qKh38yAk3UCshmF+oRblqxebfTptf45z4kSdtE7OrnESclqvWtaiXqiUS7lFJI5j0EUIprHQWJFWLWYv76zX2pw+DXloH+tWIeUu4wrwCkmVovZTfX/0pW4aJqgJa5E2rYJPg+ZzLAwJIY8J8U6r0RuVZHl5aXBc7y9N2L3qd5MAJtBMnqsTu0D30p+jMhZ6xfYeHnnunPU3ISdx33dMa1Fa+pESIrk+TbaEgENzi2OtRY3RyiSTLfeqff3pO9cxuaHc9q3qtQ9C9qCkVShZgcHXBpsApRQaPzh2J04evh0zU9WRSoNETViLtBFCPg1aL0JamhDvtBq9LHwUBl3yyaS5mH36DI4+fy7qVGjC9l7TpnewaUxKQuCz9+5ufyftu+ylP+nj08QVmaw7yeuEpKUIqQcIrPa/LXhmslLGxnVrrPfrR+oRhVqfqEVdJTRFiR6JHvv+5hdqxgAfXeOWVR/49iCXFjhEAxyjUR2laGsKYRppnJZ9A6dXIS2WtKaBXnOkmRaPB+cWcfT5c3j4rlv6Mjl86v9e6z1maXKx9e+KlO22hZgNbZF3vUS9jmLFgzS4NlHbhuJLSxE6ZvS1yBY888hHbzEKPrb7zC/U8Mhz59rRk5s3lK1zM+2ByqU9HIUNM4aQFCVAb3niQpN1hwhBvR6ifXtWyJ62bs1E+/lN43PQB/08oBDWIz6JvFchTeFLzKgvrhvKE9i8oYylq412dNahuUUcP3HeWkbpyrvXu9oQI2TYTkGXrjb6dhIOmeRpI2iyPnnlIfTquXpMC7OtP03CXEw+sSItjFm2xRWZ/NZS3XkvfT4qQlLemK4JdG9Od37oBhw/cb4dLXnPrVVn6ov5hVpXaZpLVxuY/XJ3qSxTDjLT90xttwmtel7DcdGOJdeMTYb8bbFRiDohfqq+ayiy0GBObig7o3Zde5rp3bzTWMm8jUVEyAxK3uTN9PS0PHXq1KCbkRqfAGUS0pKO+7bvAOhaXIGmT9HBX73JuJn6rg24T8kmPnD4BaeDeT9MI/uOvRSs/v/BsTtzbYuPkH53EfqsJSGwIqWzjqktsatrU8/qObIkj7ZMPfqicWOZrJTx7vUV671s/eOaB6b2lycEINCxOZk+8z2na7wk22R75s0bylj4zB3BbffRT3Opj34dJHz38fWT/n3fmptkslLG4sPm/kszXpPsOfqiNUdd6L7janMWbewnQojTUspp3/eoCYskzWR15ehKnmZMJT98jtJJAQxoLtCmgs1J04DNbLVh7RqnGjiZdNJ3CuqHP1Ea9f+g6FWzFho9tyKlU+C0ja2XX70QtLAVKVFnHm2xRSYLAee90rgZ2MzpSUyfpc2Kbvo32zx2ze9YrUzyvoPUpqbRsKRtr2kvSGqEyiVhTMSbbFeoz5nCkAGiTRZ1k11JgpMHhNDxovtfjqqvKoWwCLJUh9pOAqaSH2kHn81XQDcNhORb8Zm+akt1lCeEdfEA+iP4pFH/D5I0PohAXGqLvAMyirQw5tEWm7B8yGGmBNKZm3tNS5EmK7qvTVnce7PlgKbuO2gzU6zwbmvvqdcvBmmPXddaqjdQnhDWd6a3y3TodK0LSx5TYVr3CPUMNqqTla73EDMn1fOOqq8qhbAIsqpjZYtoSV5T/d43+GLLGoXgcxZN0liRmKw0k8omT0T9FHySgs2gT9h53Pvo8+eCBDD13l0O2b0ubDYN6CAWxrwWaZOw7MtTlCaQo5f5CgATQlijNmcP7LC6LSQrbGwoT+Bqwh8HQHt+m3BFD/veRb+0qbb5aFs/bYKCrb163WFfZLarwHpjRWLD2jVYutowzvPaUr3dXyon2KWrDe/BbJOj/3oJPPLtD/t3bu36LEaLp/phVPPRMU9YBLZJqU5CIVnqQyNa9Hu5cqLMHtjR9BExUJrozjcVQqizaJLL9QYWH74DTx7cE1wRIG9mpqpdOWb6QUj+tzTX3HPU7K+TRK/WMPv0mQ7BWDlkzy/Uesq3M79Qw0/f6Q7oKJfEQBbGfuYO8t0rJrefohcBTP3eNsZmpqo4/vHdHYLU5g3ltt+oPk6TDtGKj+y+wXpv1/vwvYt+aFNt8/Gh+bNdGdkVNuHd1q5k76nIbHW/B+cWMfXoi3ho/mxHPi3bPWz3F63rqZxg7zRWsHlD2Xswu3LtelDllNh129dPc//+za77xuTCU++hlzYWGWrCEri0Fy7pPeQkN79Qw6GnFhGy1uoTMMR/6Miz30E9sXheW5aYEOiIlPQV9zVlNw49teiTZdgnRq9kfbqPcXzWHVX3HXvJ6jOoF99Nm47AdO2NCX/CftHP3EEh94qdB7ZM+ZOVMn7yzvWeUxGY2mOqsGEWwdx1dH3vw/Uu+mFmss3HL33rTWvCZJvwHuuLpXPpaqNDY2ZDvT89SlWR/G29sRy0Lqg5b+uHtOu2732Y7mtyHfnxOw0kl5PkgW4U9xYKYRo+3wRbcV0bSb+q2afPBAlg5QmBq9eu4wOHX/AW4FZts6m2V2TTyX7hM3dg37GXnEKYfnLVcYXqJ39LmoSc7mPMlaGOrDFaTPVvamyp9rjSmdieRWep3ugau/2iX4t0HqZmm7nlkY/eYvVBMxGjQcryu2nffT/MTLa22wRbCbs/WqwvlunaLjoEj4yTF2ShXQxJaRNyX3282PamXg50RUqd44JCmIZPezEzVfUKIzpJvyqT1sBEmgSjPtW2/qcN2yl6ZqqKo8+fs5rBJgNykY0bttOhRHPBSS5ctn52+Y4kUVpMtO7h62/dRySNc7TrBKybfFzXGEbyiqZzaZNCxwDQ7JfQDShGq+PTTNnqBiYjqU0aQ9tzZ4XtOW1+eFXHs5raGyKIhNJYlnhwbjHKR9CUMsVEr9pFWy3ke26tGqPxQ+9rW6sueyw3Me0s6lqUW54wIcSfAvgIgB9KKX+59dkWAHMAtgN4DcC9UspLvmv1K0+YLe+KnlcqND9TMi+KL6eLK7IQ8OcXcmnoVCmckIXclkPLlU/KlIssNM/UqOIzH7pOzrowFWKC1MdajNmyXBI4/rHdmJnKLq+V7XmKmMcnLbHvyvSeVP+7ihv7rmHLF2abkyb/mZjr2n6v1pVQbdAg8sjFrl9p2udLPqoTozkLQSXw/eqZt53WjicP7kntDuHaP9Q4tgV/qHXGRtY5wIqQUyw0T1iejvn/N4DfSnx2GMDXpJS/COBrrb8XBpu0rn8e4lBYEqJrErtOAiKx4Jnw5Rdy/Vo57O7fudXbdls7bU6RL796wRoplKVT+qDopSD4ujX26eXqL/W+jj5/zivgTIjOLOwx+ZqUrwbgDjqxPbcaE66oOde1h5VYR3JTn+hRdCFzwzT/jn98N45/bHfwnFSFnPXxDMB6Xb1f15e7x3KyYHOoUKFHlPcL2/r12MyuzJy99SCgxYfv6HqHikq5hPv3bnNq22K5dLWBuW+/iSvXugNlFJOVck/+qD5riy34wyeAAdkH1RQpdY6P3MyRUsr/VwixPfHx7wD49dZ/fx7A1wH8UV5tiCXEN0Hf7GyDUq//p1/7U3OLRqfXWEd9HVepEJ16YxlfeOUNTDpCmgXM4cTqPkn1u+sdmJxHB5HAsxfSqrTTZA9PEupsuyLRkVcudpHx5bUC/M/97nWbKzfa1x4lYh3J07oBJLH5XCU/s/mPqX5MjufH797VpR2YX6h19Kup/FiaBK2KQWyGrvfXi3bIF5jh+l6oZSWJSZPmcndRvoVpCOln5dqQ9l1mbZIuUuocH/32Cfs5KeXbACClfFsI8f4+399JyEDQJ5TNZm/qaJdflc/2b4vU8SXJM7FUb6BSLuHJg3tw6vWLHZE6EuhKFKvfx5asNZS8F96sHTHTRjj2sjmlod5YxoMtfzxf5QITqkyNy0Rie27fsyYPMcPiLOtqZ6wjeYjfVW2pbs3xFYvL/yl0PIeM/V7mcxE3wxhiDmguwWT/zq3etVTtD8k/Y7jn1mpHrdGYeRfSz65s/KFkFVRTtNQ5PgrrmC+E+CSATwLAtm3b+nZf10BITryYIsmAvezHspSolEvWzSwZqRPjrG1CNweEaKxihQrbZr6pUu5ICpnlBvzQ/NmuZIm9OmKmVWlnJWxOVsq48u714IAOW+WCCdjTDkisjksJt6+K6blcz5r0dyqas6xN0PK1M/bUHlpOK6t3YRMSbfeP6Vf987SpGlSeq33HXiqsEO4jq8TdrrQfQPNd6QEOaZz/JyvloCAg6+8DDnaubPz9pmipc3z0O1nr3wohbgCA1p8/tH1RSvk5KeW0lHJ661aziazf2ISRkhBB/gQly3FB+ZDZ/l33HQixz4fw1lI9WMiIESqqkxXcv3dbl32/PCFw5dr1XPzE5hdqxtw7vfqehPgIpvn3EJT54D3r485JjRWJjWvXdPi4bNrg9tnSkbCPU9Nz2Z5VOcD6hPlB+AcB7mS6Ie3U/X98SYB1fyQA1uSgWb0Lm/+TzQcppl9j/WMFgH0f3NLx7LH+cEXE5UN5aG7RmBTW5FvqWluT7+qLr7wRLYCVJwR+/E4j9bybX6gFRSgWSbOZdaRl3vRbCHsOwCda//0JAF/p8/17wpVr5v692zB7YAeOnzhvdeK2qZCXWz5kn713t9c5McRZ27aJ6tw4WbFOHKWxUs8xGbiJK82HcgxW7ahOVvCe9Wu6gg+y2nRcgQm9aKVszqL7d27FnqMvYvvhF7D98AuYevTFjr6OyQZtQpmNjp84H21aBJqLjS4gxJ5SlWZWx6bh3b9za5dQoWs69PdSJGdZl6Dl2mDTBGgAq3kGq5MVpwN7Vu/CJCTaxuVVQyb1EEfpkMAMCeC1H9Vx8vDtxmcflBDeKy6hw/SMyUClQ3OL2H74BUw41urkdWKjKQWaGnCbIj1krB19/pz194pB5og0BU6lPTwPityEMCHElwB8E8AOIcRfCyF+H8AxAL8phPgegN9s/X1ocHXiF155A59++oxT02M7iarPbSdY3Zzj25Qr5RLuu+0mpxCgJo3NCf8n73ZqrH76znVv+SMlnOhaOrWZzx7YYRUEsth0fIWL02Lqj3turWLu229aywAlf5cGJayrsP9Yks8c+w50zYlLwzu/UMMzp2vWzSI5B4q0OLoEQt8Gm0aDE6rBzvNd2IQm5XSvP49vLdLxBWb48hQWMWLNh0lIdc1V2xzptVSVCwlg2SFBhYw1136THBe9RJKn+a1Nm23KAmA7GBaBPKMj77P802/kdc8sMfmL+Hw7kgNe96+aX6jhyrvdzoKm06XNtHH0+XPONus+ONM3b+nIWSMEugrJ2k6gyedQxbk3rlvTER2ZzAPm0i7kWZrEdm1TQEOsY3iyP3xlgNR39d/14sPn89NKYjqVhvokAavvLMRJNsRXUJ8DRSrA6xqPIe8rJtp3fqGGTz91xrvhJqOT8wpi+InBadn0PFmNATXH+1GeqF+Y/AJ7dRHpJ1nMO5VPUtW01Q+moQmMH3nuXFdes1CfNdt+8/KrF/D43buM+esG7YdqorCO+YPE5pj7+N3NnDIxWfPfakU9mRb1zRvKePiuW4IXctep5IG92/DYzK72330LaGhqC4Uqzu3CFhr/1lIdTxzck9sGbCsjcv/ebd4oz9hM5653VluqY+rRF7v6tB1VZ0hkGEKoIGYbTyFpVfR7PRhY+SB0/OjlkU69frGdWbskRDvHWRYChymNii1hsEsgVN8xbRCm5/K16cizZ4M0Hnp0MoDosdprW9JopHy/0ed4kYTwUFzj0nRAGxZBbH15IqjCyaSl3rDSprrS8bgOKg/Nn3VGhYYcclyaVdU3pj6pN5bxyHPnKIQVGZdG5+Th2/Gppxa9dnKF2tRMbFjbfP1qoOilPpITw+c38fKrF4I3sjSpLUJOq66TbtZ5YHRCrx2bcsIktPmEIWWa1Nul7p1GAFOYflkSAitSRmv0QjQzvk1/fqEWrKFTY0eZL9V9l6XEM6ebpoFeorfUtV1pVGIjHJWm2CWEhcyJ2Mhi3UcqTXqUXtqSRiPlOpgk17E814A8iD20xWick2SdQd+HqSwe0N03j3z0lq7DY3lCtHOO+caUSVBSgVQ+fAJ+iGbVVd82q5QwvUIhzIDLMXd+oZbZbEkmT9R9gZKT3TcgbYkY1TV0AW0iRZ4Zm/+Yju+km1UeGBMh184y07mLpGnSdY9esAnsIUkkAX9JJNem76vSoND73yYEm2rOxQocsaZRwD9mXH3m0uD0mkImpOi66756vwOrG6urv9TzxBzkbOWKyhMC71nfdF3QD4/6dZ9IWT4nS3zPGntoC9WgJkmWeUuzPveC0gzptSd168/xj++2viffujYhRJewE7p2+A4FIZpV1yGhKMnDKYQZcPkYHX3+nDXnUiym5ImK5GT3mcJciRiBTlNYmgmezGfjWsCKetKN9UnpxbSQXJxcY2pyQ7ntrxd7T5OwHXJ6V//tM60nnyNEwNhQnsDVRnOW6OVuXNHFIfeOaWev3wPcSU9tTuqh1RJKQuC9lTVGFwOVJsT0XiSAPUdf7PDx3L9za1fNwNpSHbNPn+mqAWlry+N3N10ZQsZO8hl1c/lkq2airmlJtqO2VMeDc4s4+vy5YHeMrAmZJ2kCCUI0qIC5dmivwnsvmNqrW39MQTkhwpQqmQeEKxSAMDN1yH4ze2CHdY0rSkBIv1NUDAWzB3YYI130xJa9UimXvMKQPkhcaQ9c16ot1fHIc+d6MoUl22KKSjk0t4iH5s9G5U/qNzH1yZS5zUR1suKNfEwKdrb+kwDeaazgiYN72mH8sSSTRIbmBJqZqkY9R2iEX73RXe7GFToek5cspJ1ZfA+wj5fP3muvhReikVPXePiuW6xz2rU2LNUbuHS10Z57X3jlDeMm2liRXgFMfx7b2HnkuXMdkWumNDlKoNi4rjsVja0dpqjMfhEyT9JG8/pyf1VbmsDXtDUyq/yPWWMzJ85++UxwW0Pfq2LzhrK3fqeKplR+yGr9NPnDbjDUPQVWSy0NGgphBmamqrna533JExX6YE2mPdBzcLkSvQqYTzk2bGlr9LbYzHRffOWNwoX/6iTD7lUdzUNzi12hy65T3tVW0lkbpvIY6t6mfurQWKbMMeZLA2ALz3bdLxnWHerfZMsFZcopZkupEuuwHfLeyhMCV69dDw6Dj0nToAg5XatrzExVcc+t1aC8flljeh6X/4x+4LIdRF0JoG0MKk9YiJbLNKZCxpArgbHtcNrvUmdJJgLWfcXR5895hfskeo49UwoJANi4tllOb+Ezd3T4ryZTV7gSLZtYZ1vf+j/tjNAcaaFqMUVMVsodtnMTNifLSrnUseidev2iNULEVXPP5AdkU7nGTBWBZjFxU/vfvlzH9sMvWN+LuldR7Ow21ObnM0e4hCyXNtQV8TozVXVGkKrvAH4zYRJfGgDAbHJJRk4qM5gprLuXTaK2VO/KKSbQrGn32MyujpQqaczYJtOEHh25yWAmC3H+j/Vj9JmUq1qQSjJQoV8IwOiTlabuqI4ag7HanEGYhUJcE5JjKmQMhaQiMrly5PkObBGOOiZDicr9mCw151v/bP+uBKZnTtc6fOBs8922Rq8vTzh99ZLv19aeopRaohBmweb0p0eFmJxSdSdLX8Sjq26Y7ksTUhfRJRyFIhN/6qhJ6osQLIqd3YfP6TamSK6eQ01FvNoIXfxjhDBfGgCdkHxQtrDuNIWDFSafRYnVOZBH0Mb0zVvaaVv2HXupayPqNdrQxOyBHZj98hmjpiCpIR2U9sN2WOpFFtTHYHL8lSeE0zfNpG3JI0eafs1NlTJKE6IjJ+KEQFvLZUpxcvXadWvVD9PBTqEfzGyCRa8CsI1ySeCWG38GJ79/Mep3JSHwK9s2dUUt+9alkDGkcnmdPHy783u2Ndo2Z1TgXGhEe1Hy01EIsxASwg6ELxbqe3oVe5fAculqAw/OLVoHfXID6SU8OhbXPCvKwPbhM0fECBtL9UZ7g1eL6qnXLxpPeiERPbEmXd2kk9RsuZ7RhsuB3lUI2kZs8eg0+DSbLjNtL/c0RSPaJkhjWbbntEtj0A9M7yPGbSGZvFmN74fmz+Idra83ri3hX/zDpiBsiho0mZ3zKPSevKbpWVdkZ+qGZIoTG+pd2oTqDVrhaJtgsW7NRKq5pdi8oYw7P3RDR4DG5g1l/NIN8QIY0Jzr3/j+xShLymSlHFyf0Rbw04t2sCSE1VXGpCwpSn46CmEOQk7nId/J6/SjD1KT0HhVU53nQZEHtg/bu1d1MnvRLKpacS7NpS80PhZTHixb8siQAuSm3yltbtJ06SL5m9i2hOLTbLqiU9PkC3KZSkKCYHqdlxvXlnD12nLqdAam9x6q6VQWgeQ7MyXgvHJtGadev4jHZlZL22Sdzy+EPLWO6l2G+Jm5iks/cXBPlzld10SZEAJ44t5V07KesHt+oWZ1fwghZlSp3GGh0Z2mgJ9e98dlKa3vVwWOFDFqn0KYRqwKPDRDt21ReafHRUFPgmnKwePLStwL+uaaZmCbSlbEVBDQr5OmDba9Rn1u0ywmTRjW6yf+Xm8s49NPnenQhNramVY7FKIdDRGUbc9+9VrT10U3I3zg8AvWxfrJ1ji0+cmoa8YKQTGnZhVY0KsfY/KeV69djzKV2EibpHNyw1qce/T24JQYOjb/JFc7QjawL33rTeNvv/DKG/jiK2+0f+szQ6VJDZEkptJFL+jvMsTVwJfQOvlep2/e4jQBPnHvHgDo8tuamari6PPnck8AK4CuMeEbj8k1KCvtoApasx0gfeNuUFAIaxGrAo/J0O2SztOiavzZ2n3q9YvtbOQmfMKEy49DL++S5jQxv1AzlvCxZZt3XSet2cKmNlefz0w1S+zoGi2gGU78Xi2vV4y20ZWMV6eXTUOVydJ9X9aXJ7rqhrqwJZ1UKQUUrgjSyUrZ6SdjuqatXcnnuaL55vhOzSrC00Uy/YrJxJgcZ1mhTui2oAhfm0PMzzbTYagAZ9vAku/KpUXTo9j0dptIU2PSN0ayxiR8+A49ofWDdZQwZdTat1Is2Nb/kHWpF42/fhDXSyCpuo0+5YTCFZU7qa1fLq2vyycxNhFxvxGyz5E5aZienpanTp3K9R6207JtAQqtExbjzBx7Kp6slPHjdxrGyBbfffWFeVOljGvXl9sJNoFVH4OQAIMQYjL2h55aYvss5reu0j769dNoIhQxm2OlXMKEaJp3fNdMRu8mo3J9+JJG+iKE9fuFzhPXRh/yfk1tCp1Pep+nfe+r358AYE/CbLs3YH5W1zMk56LpcFOeEDj+cXNes5C+sY2dXsa9b37a+qHXBLlZ4Wq/baP3Oe0DZhcF24G1XBI4/rHd1nka4ypwaG7ROMYEgF/74BajT1m5JPDh7Zu7/MZi1xoAmHr0RafAqCow2L6TtKCEHKTStjUGIcRpKeW073vUhLVwhfWbCFWNx/hr6KfiEFyOtL77qoLcq4tDZx2AS1cbeOZ0LZNBmlyAYpLUuojtMx3XqTWm0LFNaxSCyaFfv6ZpIbFF3gHNhVGI3moOhmxorudMCgYxfami0pLVF0I2V5NPTcg4CCmrFEO9sdI+vV+62vBuhrWlejsDvi17vk0QM2qWkrmPDLmQQjKzmzQ9Or34WPnGRIjfZFZtAVafVdfYuA6KLnO+zTrgctoHurVZqqIAAKOP4caWs7/N5ytk31FaLNs379+7DdM3b8G5t37S5TZy54du6LISAPG+e/MLNa8zf2NFWgWw8oTocmEx9cG+Yy9l7meYFRTCWtgWy1Kr9lVyQcjDzyB5KtY39s0th/FQ05dv8VeqfdcClhyk+uIdohlLW4Yj1Fk7ts+SExUwL/SmCetqn/L7swknodpQ/X0nFxL1PK4kiRvXrrHmvgkVhnrd0JIagph5YjJZhbbb5FNj0/TYCp9nFam5VG+gUi61feIAsw+k/n0XqhyQq7SMGoNd2eoTdUxDhOwQTbLrXYWuPao9pjkY4+rQS7/ZnvUDh19IfU3A3d86taW6dc651noluLhKa7n6QCUqdc3N6Zu3GA+qD991i1N4i+mPo8+fM1pyQmmsdNfpjWlTEVIqUQhrYRuwqvZV0uZ+z61Vb+RKDAKrRbLVwnS5ZRNXdeJCx6rKVWZrn376D01XYNNmhdaXiyGkWLjeBtPnofUTk232mWhs/huu97h2jUC9EdZ7pnuHvsulul3zsqlSdgqmoZuGD5XQV117+/viDyshUY06tj6xaTtt2t0sD1b1xjKOPPudjpQ26r995pckyuHY1jdKixiyKfqEbFuCzuT7ckXQusyrSR8pXbNbW6pH+YT62qJj8nG1pcfw1UR0bfqx86gkRCpBQAmytjHu25+kdCeErk5WnFGqrjbHlAPKInrfVlpJX+tsPqNFSKnEskUtbCWEbIWxX371QlcJHEuJqiAkgGdO13D/n3wTh+YWoUoy6HXiQlDFeB+b2WUtc3TPrc1T8wcOv4AJT+2GUI3Zp58601FaoheNiiuJrU5sn7lSP4TWbrvnVvMJ3TWZk6ZegWaKARMqZYKOqVafDZtgulRv5p0zlfpQfie9CmAKde37/+SbqXIUAasLq610zOYNZW8poZmpZtmhSW1TWO+YpKZ79VLZpN5YwUPzZzs+m1+oRW08SlDwbdS+3H0hBwy1NjxzumYcJzohdVjV+7eVczoqSAAAIABJREFUfTKVv2ksy7YZLpSQMXLwwzdho5ZI2VSfMHQNsPWF+n3MPFpuaWRj0AXlQ3OLWLdmoms+JNf/WGpLdacrjqvNV1oRz/1iomX5UJjKGv30nesolzpnc1FSKlET1sJ2onAlmVSnW9Xpib02mnpjOfWmBXSf8k0q/Rj/rNKECNaY6Zqx2JI7SUJPhrF9pjIqm/IVmVIOmLAJiDHJciWAcmkCAstdm2cyZULsph2DLpj2WuDddO1exrJa5GP9g0y8e727oLh+bYXpXiG5mlx86VtvduRuiskBp2sU05j1gVUNe6gJMtR3JrRfXGZF27iOHe++tpg0cu8YFuvQg6NNAElz8Ny8oRy1dih/LH1MKvN3shSV/u5dmtJYJIAr7zaFGpN7RNIE7sIVeCKArkhXE8ryAay6hiTfZWNFolKewPt/Zn3hoiMphGmsW7Nak0pFXIQkmRxE+RE1QJWpMnRQxbR1ZUW2r5dnrp0koSdD2+Lr2rD09B1pUg64VN/1Rnhpn8t1u3ZTv8cjz8VpBWLpV5/GYNKoAKv9nKwO4CI28actV1NILi0TybEQcsAwmUxnD+xIdbiRAF74ztvBeZtifGdiU9Qk00hkiastoWMgNpAjSRqzopRxwT0b1q7By69eiHYyz3r99rUz9F245tMTWp5BfdyYMgLoz2+7d72x0s51WCQohMF9Utq/c2tXFEgy70i/N7JeEs/FLBTqmV3JNmMISbyn8p+FYlt8bSfLemMZX/rWm6lrICYFRJNmMSQ1wuSGMjasXeMV8LMyERYdm7M80Fs+uCwccvUxFppyQ1FKmPtDNkOTyduVL8pHTMH5NDm6QggpG6SYzFhACxkD8ws167x1jU3992mqF+h5CXWBI01Uvuvf+lnWDrAnEk++P1c2AFuQki1oQj2/a465iq8PCvqEwX5SeuS5c3jmdK1jYgo0F0kAQf4DagmuTlbwwN5tnT5kpXiPk17t2DGLqYoyjPVzSKL7Kvh8FCR6nxTKF8VGWgGsXBJtXwzl/2by1wq5upRuvxrlw+NjslLuuka/mBCrUbvJkVwpl7Dvg1uCrlMpl/DZe3fjB8fuxMnDt3f1v0uT4cM23tMKFft3bo3yE1uWsu0nCZj7PInN5H3nh27oyUfNhJRo+4fuO/YS9u/c6vX1CkGNX9c8sfHIR2+JupcP1xhQ7XzQkStLjc3ZAzvwyHPnsP3wC9h++AVMPfpi26fSldImpm0zU1WcPHy7dZ28cbKSakzr/nl5k0z14/IvdI0rVe0i6V/me/7QsRq6huQNk7Ui3l7uKo+g4yvDE1oyBOjO25M2++9D82eN+V1MPLB3WztZa1qSWrtkRJTv+yZCnz1Wa+FKYGvyxegFAeAHx+4MTixoQpmtgPA8ZWnL5JjQE4Ha+mS7J9Q/pFSVbX6qd+jClgD1/r3bOny1Qugl4le996qWj8o1Np9M+PfYnuPXPrgFr/2ojreW6lhfnugKAolFRda5MpzrbTKlrOnFj65SnsB3//k/MP6bba741gJb+bZ9H9yCv3zjsredrx270xpkUi4JvGedPZGoi9gEtPp8j01mm/RznPv2m5n7gSrU2LWtv8kk1UtXrzmTIetzx7Y+qoSuyjXHd02d1zxrSFqYrDWCWHt5iCljQsC7uehqVtdmZRJk0phn5hdqXZo9W9t/97bmJtVLvhzrCdrSgJATd8yzx6jgbUWJdXz5w2LQHc9DEgua0M1WShByCWMlIXDfbTdlVk9Uz9FjMwv7kg+bHKST9GIim5nqLj+lIpGnb94SpXXtxfdT3bu2VG8nQfb5Lqr22+4tAbz2o3p7bYg9dJhQkd8hByFbyprQQ56J9RYtoWnezz59piPthG0tsGkWX/mrS0FZ5R+aP2sNMmks2xOJJrFVyDAREvgQchA1vTdX/7iqsISga2pd5Yj0JNXlCWF18gc6586RZ8/i8bt3dZRGUn5iuqkx1MSXdBcYBNSEIb5cSKU8gS0b13kXPOVLYHOgDynlI4CuqBdXyZ39O7e2fZ7UpqtO/L7klaqdeqZvIezFrpNMegIFXKWAgKZWRL+3qSxLSCkhnZB3XBICn73XXNpFJ6sII1OfprmPK9GkTXOUR4CFK7t6FslBfWVsfJrRXspb6fi0ejGose16N5OVMhYfvgNAWKH0LMenT8OYhcBnu/f9e7d1rWExGvlkv6Z9L2qMudas2Gv12/8otp+ePLjHWsYICNOk65G2ofdWfoAh2vxk/+45+qLxd6Faf2rCCoDp1OEaPPXGCra/r4KLV645Nxc1cfUBkiyw7UoVoRak5MS1nTCSRcSXpWz//bGZXdbfrUiJJw7uMZ5sY9YeW6g0gCC/CXWSMSWCBRBcSkgnGaZtYkXKoMXRNi5iTXy274YkitSxPbNLc5RHhmhTpnuFSROVpJcyNiGa0bTO+XlG86kUN4A9aeZSvYGpR1/0Fi9Wz2sdn5aDlO3zEA1jXsFI68sT1jUslGS/brJUG3AdMPUDoC8q1TT/k+axXt1I0hI73488e9aa2HSyUsYjH73F6/qg5/gz1b00cbneMiMGCGHJZ7L9ppkKyK5hA+y5JvsJHfNbKIdI5RysHI5tvPJXl9oO+rGoCD2XALd5QxlPHNxj9FuJdSr+0rfedP7uRkt25DSogIYkaa+vnCd9vw95J7bvSDS1HCYnUB2bU3WaM3LSQTU0UaSO7XlcDv8xY8eWUNaGzdH15VcvBCcT/YChH1wbV4jTfhpH5qRTcdZRqpOt9WVmqupca1SiZtfhRT2vrd/vv21b1Oe6S4CtX3xmnDRGnkq51JHTLS16ZN6+Yy9Z+66yZsL4/E8e3NMRIOJ7VlPPvGf9Gjx81y0dwSYmR/VDc4vGtcc1H2KI3SvqjWVI2aowkOBKK73D4sN34MmDe6zvRXe1eM/6MD1PzAEx6pkksMGSoLkoyVophBmYX6jhp++4UzIsS4lnTqfPCuxTb7t8ZWyLre9epsiu0IzcMSzVG12LRi/Xf2up7vy9nkHatWj5ItNs2cEVKsIoCz+CpKAQEz0GuBcQPRIqmal89sAO4wJrolyaCP6uwtRPIX1ni6LyRViFaLlCsrv3Es1XSVEqQ03/kLUmBCW8m/pdz54e8nkyyanp3bvWr3JJ4P5WJHgMj9+9q6c6gsDqeNpz9MV2hQgbVxsrWLdmomOTNlVUuO+2m6LbcelqA7NPn+lYS2x+fYB/zM8+fQZTj74YLZSFROMmWao3cN3QvyoJK9BcYz57727vvLLVsjX9JkS4Mq17rkNMY0Uag1VMFRMGBc2RBo6fOB+kQrUt0hMC3sXEl9TTl1BStVPXDrh8Fx6aP2tNt9FLRm4bybb34ovkMsWUhOiqk+YyjQHummm+pIczU1Uc6rEigEKvyxkbXeVbQGyO8uqzI89+xxtJd7newBMeH5EkpoXUVWRYOdi6tFmuxJQhTvs+J2eTSTOGNBGJKj9U6FoTgnJaNvm5ucZDmiSnroCLjWvXeP1Qk1QnmwXY0/hfJSNPYyIzk1oyVVHh1OsXOyJE931wS3QFiMaKxCPPnfOaxRXq3ZqqdzRWZKocVyFrnglbF+jPEBI84Jr7ptxrvqhHk/n2zg/d4DRZmx5lw9o1hRDAAGrCjPSitREA/vjePd7Tx95f2Oz9Tmw7XCe2L77yhvEUpiKH0pyYXCTbPntgR+ocR7ZFWeWXsmWQ/vRTZ7pOjCETz/fesyr6KoG21iUGtWGlZWaqii0b13m/d2PrPqH+UDbtUm2pbtTAqmAIl4+jT9NlG7e1pXo7n9Mtn/l/AKDD3UB/f7FayCxQYyh2jrvywplquIag+mn74RfwwSP/tm0eswlPtaW6M3nzZU2wCVlXyhMCV69db9WyjRdIlQB28vDtxrUglnpjGV985Y0OTdRfvnHZ66JiQhfyQn3tQg5kMTmuZqaqmfk+2fKa2XL82bTQn713N544uAcAcGhusZ0TMamZPf7x3Vj4zB3W688v1DD37TejnyMP39i0UAhDpyliz9EX4bI2lYRoJ101MSFEu6iqy5Lz2o/q3uR5EuhYFNWiajMTTN+8BQ/s3Wa9lgm10M5MVXHPrdXMkkEqAUO1eWaq6tSm6O9UmftcZj9dnWybUKqmWHIz8pkTTVnxdVPV9vfFL2i2O4YuugqfH0OoL4lvERJYTZYYGrFkM2UBzfEgLN+1bU4Cq75TSZRgGJKE8sq1ZXz66W6BXLUzr9qcNvQ+dD27qeDwIx+9xZuI2JYY00Syn/SgGBfOjPdan5lM4w/s3daRFV9peSSQuv7uW0vNurBZafOTa5XyleqFEIE0xtUh5ll7OQQr0vhQ2VwjABj3MMB+WDKRVpOc1UE6C8beHBlaTiMZYmwLvdcjIssTTZWrCVMBcNMJzhQp6DITnDx8e1SeHn3S+xyoTUxWynj3+oqx7Um1uc2EUZ2sGAMQXDnKLl1ttDVILlOnybzoypWVLJtkMlXZBBiXiVkl1ew18a0rmiomh5rrnenRXkqLZU/XYg69t/m/mNJCzB7YYTR5SqDtJJxcaK9cu94uxq7+59LeLK+Yiwr7tAkhrgUhJBNOqnbYitCrjcoVkOAbSz7TurpO1lrA5BRImjxVvsIs2VQpd0RSm6iUS1hfnkgtdF+uN9ppdELZnBBIAXffxZhiBdCeAz5CopR9mMpphRCaCzFkvCbxHSYnBFCa6IyQLIpDvmLsNWEhi5DyXUn6F+kSvukE01iRVq1asraWKv7sQg1Sn4kmRsrXJ32sirY8ISAEnG3X1eb7d241fsf2ue85lP+GqdSKju57te/YS17/ASUYq3ImoWWJVlq5zUzoSTXTsHlD2XkyVDnUXL5VupbsyrvXjZqWzRvKRmHI1LtJ51b9+i5TVlJD59KSLtUbxggr3UlY4Ru/pn/3CTLvXR9eFqpS7o62A1YjnV8znO5dQRQqkEJFjh0/cR4PzZ+1mnhNKA2RTTuah1nmskdzmrXgp69DLtaXJ3Dnh25I7XZx42QFD991i9G89sDebV3zqVwSePiuzhJMynyXhXlQwn+I0HlsZheeOLgn9b2TSW97ieBMkzbGdD/fHrEimz6KtuCTIjD2mrCQRciWRyrkZCNldx6ZZG0tV64wU3t9zsim07VNm6FPyBjnedH6v2RuL1ubAXvm6i+88gZefvVCu5yLXl7D52SrMny7kipuqpSDS85UW+kS0pSncb0/9Q5s2jIBez4jwB1l5MvBpjbipMZ3AquaHhXg8EWLgKq0ODYH3Jh3ZtLQ2bSkAvYC1Mm56xu/JjOzDxWc4MuNVJ4QePzuDwEIy2SuY3OON2k29QOEEo4l7ONKaYhs2tE8kvf6NsZYwW9DeaJdPsyIcBcpV1y62sAzp2tdZZm2v6/idbpXGnKbMzoAfPXM2+0xYivH5SvQHUvsu1RjLU0C21prHQnJz+fLhxZbBcN2v1/Ztsn7Li/XG+2kx0Vk7IWwkEUodGDYsEUkpimDowa0yYShFgPTQmFbaHQtVGiZn9h6aSFOyMkNRpV3uefWajt7tg098aUpOeCVa9eDnK/VOww5qZsE6/07t1rV/eod2J5DAti4bo11o3dtbL5ns+WBW1E3xmrKFVuiRoUt03+sdiNpenCZJG1JNZVfWMjGVpoQXSaIEC2C0ja4lNRCoKOGpvrd31x+Bw/OLbZzePmEseTGZYqSM7VPJdE0rQkmDZFy4D80txidhLY8ITpKBSUJMfXECn5r15Sw2VGhpLEsvdHmimRZJrWG+1AacqB7fX3kuXO4cu16xzsxpRhKc7jzPVda36a0wrfPJebTT53pSkSeTLqt5qpNOaHwVTupN5bxjYCI1SL5f5kYe3Okz1lSj9xJqlzTqNX1iMTYU4wapMqJXndgT9rrk1Err/3IfC9dOxWaB+v6Sni9NPX+tqc4eakF877bbnKaXnzJAUPru/kc/RWVcgn3J5yL15cn8MJ33raWC1KLi80UoLRMNmwbm8+xPCYPnHI+ts0Hl7N3mgVd/43LJGnbg65cu46H5s96k9wKrPqEpTHF+YIn1rQicFxO7j4neVOwTegcU4J70qx5z61V6zWUA39MEloB4OCHb8Lxj+1u32eyUsbmDeVgU8/8Qs0ZWWnicr3hXaeXpQw2M+r9HrqG63PdlMg3KZSaohdj94vqZMWYi0vH5srhI200vM8lZllKYyR+vbGMI89+pyN3m/7GfDnqXIdXH1db/qNFZeyFsKRPhr6oTFbKbVV3TKJIHz7frUp5okvo0DVoyrFVDUylxXANtFAb/MxU1RpMoHD982Sl3PEuQ00FNpRGzOUYHpsc0ISe9sF1clKLxfTNWzqye1+62rA+p36KTpPNfrJSduZysjEhVgXL0NPg5XrDGW1oC41Pk8A2+ZvYazSWpbXyRHWygicPNlPFmBJiAsisFJHyT3Ntsr6UAr36SSmtojp4zR7YkbnzuzpA6vdZfPgOZwoBHbWxxlYfUKlSXAdENS9D/J30uRC6hl/RNvLQvkpe23Uv03pfa/kB6gfuJDYXDx+hB24TtaW6NWoZsAtGtnx6k5Vuf9cs/QaV73BRBbGxF8IAdDjAXq43sGHtGjxxcA82rltjPOGo/FNp1ZwTQmB+oWbdkNdrG4dColl+6KH5s14HbBMxpVvSPpcKoVcLtOn9xeJyuDWdvF2Cre3klxTkbP2iypmcev2i0WHfhn6Kdjli2+77yEdvgQ3Xwv7e9WWn8GdiU0vgO3n4dqv20XTPNAWOl6XscLZNew1bG31JYDMofNBxv5DAgPmFGvYcfbGdw2zq0Rcxv1Dr2UE++fs8oh5N94khTZuSbhauLO1q3Crh23c9IHytayxLPNjKZxWq9U1e23av6mSlw2E+GZ2sH7iT9NIftvcZwk/f6Q7sSYtJKO/luUyCZUxetX5DIQz2vFuuMGJbVJ6KMHOhfg+Yy4zYtDmqmG2aCRlSukX/biymMhBZOJ/a9mUBWJMDmkrtXF+RuOfW1aSFaqKaBDmXoPTQ/NnogsI/ebdTHW5LcOi6rw3XJqJHqZk0vqY8dvqJP1Rwn1+opcpBNNlyGlfzLg2u+nU+7W9arantfi7tANAUcGefPtOx6Vy62sDsl894fxtyf53YTSxUIO3Fvya2TaY1JWSOuHLHJcd8rFkudE1TPoghEcm6AFmdrBhzlPnqNKbFtC6ECFeNFYmNa9dkUsLNROhzmZJA2/bH2lK951qceTD2jvmA3cnQ5RSpinAvy1WnUJUDCOguv2D6vcrrZcpdlEaAcQ1cW1SPLerzU08tRuVHMpWBCHWWTcOmShn7jr3U9SwzU1Ucff5cl2mwsSw7HHJduCJ7VDH0GGw5qkzYIuVs2BzaAXN2a/3aU4++aHxPqq2+ABDF8RPno4Uom9N4LHt/YTP+8o3Lxjba5pFKJFzxRd0ZMOXFU/czFa7XvyMEjIklG8uy7YsXEtHsc2gG7I7XtvYnS3/ZnkHVaI2J/vS1ycZSvYFDicAGX9Qd0Jy/tmjWK9eWMfvlMwA654PNWTwtK61+NuWg1COSdQ2Nyx9V+bz55mIIpneor4v6v7sitlXksKnU0Aqa614IJqVFSJCYGrd6pOvsgR3G9V+RTAxbhFQVFMJgP6GZBn7y3/XvJRcENZBtQ9F239AoRR0Bv5NmzAb/u7dti9L4pDVRpan3Vp4QuHLtentxSE4qm4ZD5ahypVlILt7Ja6cVKvMqk2FLwmhboEMiCVVbQwX30GdTm486rGRRg/Pk9y9irXZyV6kBADgdwNMccnTTsOmduJ7n8bt3Of9dbWj6dU3zwrbxmDTCpjQ1H9l9A6Zv3tKxyQrRLGu2qVLG+vJEu06fL2VM7GYWu66pqabu44q60x26TRHSOvpBQ/1WRatnmXHfZn5VTTMl4rYJqmrOuOqfhhywQxI6J/cJ23vZpPmqJqN6Y/yAf+mGn+n6LHndTZUyrl1fbh+aXClAfHnqgHSJYfOCQhjcA3//zq1BwkiyU/WBbBvENs2V+l1MMVsJ4JnTNUzfvCWTgaUy2Ovavr2/sNmaTyeZfPatpbpXE1YSor1YTN+8xfq8yqdKCUemBVZ//7b+VM6ugDmvjW2D0K/teiZXNu08w6Qfm9nVsbG6NAQhm2Cy+LVvPIVqOG7YVOlIDeAqzA7YNUFJriVSAyQ36ywQorNouumduNaRmamqU/hVzufJ64b0qwmTcK6vEScP327MHVcpl6xpSGKynLuEghAtSxLd8uC6f2gZmzSJe2MoCRF18FLP4dI+2+ZiTKUMl5+kbVzNHthhTf2jV61QuCqdmDj5/Yu4/0++iS/+07/b8bm67urzrWqtTSlAgGa6nlALTlHqR9InDE0Nksm2rE5+obg0W6H+WIo0TpNZOx8+NrML33/8t/HasTubf1rSXKgUDKFhxQq9tqPL6fYju2/oiES04SrqbNrQ9fflcxpW/gRr15h9IB7Yuw0P33WL0R+tXOr2D8naL8HmZ6YT4hidpjZlqF+NXrnAlVxWYcvU70Jt1lk7pVfWTOD4ifNdfRfq8wPY/RXLJdE282U5NkwpU3xj3rSGqGACXyJi/fsmH1s1z08evh1PHNyDn7wTl6oixBc2dGM1HYqy9G+677abog9eKt9hrF9oaD+qe9jubcOV+sd0jzQHzpPfv2gd8zHPF6OBK0r+sLHXhKl0D6aEqi+/eiFqMfdpttJk0la/Cz2l5Snd266tUjCkST6bPIWtWzPRvoZSOYdGVVXKE21zY9K04ttAQt6bRDPMegIANL+O+267qaP2pW7S3FCewLpyCQ/OLXZFPT04t4inT73RdQLMi5BndC34thP343fvwuN373ImVgRW50dMlJyeqd+nOVPk4Yd4tbGCqwktalLjpurFbt5Qbo87fZ6rP/XxMdFKfKprrGLMbzZc+eN0bbAJfZyEmPeS655t03zkuXM9+V65qgLobfGtlepQBHRq7FztUfdWFgFfHdjHZnZhfqGGByNM7nq+wxhrRoxg5ctUb9Ng2lw8TPdI404DwKqN85VAi/FLVBSpfuTYC2G2QsPKFyKUEM1WGjOh+p3JidpEntK9y9wyv1BLrc43ldUBVlXOof2gb5RJ04rPJBzjNLwCoLqpuxA10NnP6plUv5kW+ZPfv4iH5s8aC5hnje8Z9VxpJlwnUl37ZupLfX7EjBO94HfsphZLSQi8txJWCcJmHmusSGxYuwYLnzGXSek2sTTfkUljFWJ+s+EKEii1UuTYBCF9DfGZ90zrnm2+LtUbbeEzTSDHPbdWMfftN51mMZvpTKH7EoWa5wU6/X//8o3L7UOHbT2MpRehwFblwhRx6zJ3usyaMWWGTIeNEJLCvysprKK2VMen5hZx9PlzWLrasI7pSnkCWzauSxVQkjdjb450nSJCBZp+FAUN2Rjylu5tZtX9O7cGlf6wYSuro07PEynNBCqn2/bDLxj7OWkqirlLiCARqvFREZe9mCtDfusyG+oLcWyxZ1PCX5dJJdTsk6xWAQAb16YrvhzCipS480M3BI+D2FQx+rs15frr9fo6rs1vWUprRKte3QHwj3PTupf1QVCNn8dmdlnNYiqHF9AsIaXn5tu8oYwnWwXUFz5zR4eFIWR+2ky6PjcTl2tIMpikl/3Dpvg1fe6am65DVhqXmhAXEh1dGxeTumYFqwnVTd9XdV197hqDYuw1YS4JP0Stqp/UB4UA+iLd28yqvSSGVIu+LXIsNrt2ErWRJSdnMrpmZqoapWURQPv0bSNUg6eSloY61yYJ/W3SvO1LrRJa7Nl2Gra1O8RcOFkp44oWZaXakkcCUsWmStnomrBhbQlXrnXf12YeM72PZB/1YjLtVcixFUsHOqs7uLRl6jqmPvZpo2JQ+QAVrtxuunk8pGBzL64bq79dfcYJgY7yca7rJ4NJesEWDWj73DY3be2tLdWta79tjsfuCUnhNat5bouiLBJjL4T5ciHpPkpJyoaiwHkxaYkkmqyUc6kQb/MNME3gtKkGBID7927zRo7lgSmvmWtzSqJC0F2TO9TEWRIiVdSSIua3LuHIF/0WmjfMh+096weafcde6hrveQpggLk6gwRQLk2gUkbXc5tya9neR1YbS+j7dkXqzh7YYY1E1rWUrvxvNr8qtVa8Z32YWVfhMo36CjnrKO33oblFb6qGUB9DEyrxri5orkhg7t+/2Y5QD53/vaZLiDkcubCZNYHVA2doG2ME3Gqin7L0azat80Vj7M2RNvUsAH+ds3ySBXegTBimdpQnhLOkTS/3tEU3mbBNdtfr2bi26a81ffOWtr9WH15nG9OiZVO520j6MKSNGrzvtptSRS35vhO7mPmuExq55TON+kwbvfgX9oJNy6LX09Sf+7GZXcGRbCF94Rv/MW4PD991izHz+QOtQ49N+NA/d7X54K/e1OFXlVwrQgQw1brqZAX3791mjGje/r5KVMS1egbVlkNzi3hoftVV4qH5s9h++AU8OLfYkzaysbxiTbyrzJAx2fh7ETzSmApNuF5HbNR9jACYNA9mac4uShoKF2OvCQPM2oGQSL9k0r+sMTmOqhNj8vSQJbFaGVtiSNucfmDvtnb0kMs5OU9M5kSXudUXURQSNXjjZAXb31fBK391qR1ppSIrX371QurTbFYn4ZDr+E7DockgAbNpQ/2+3yhnatvz2547VDtge7clIbAipXesxbo9+MxHLm2kr83AauHoNNVG1H1M61cyr9k3vn/RuC6EVuOQrWtO37wFp16/GF1yzIbJPK1wJTu2JTLtRfCIMRW68re5kpzGCjOh+TVN/qGzB3ZkFoBTlDQULiiEWQgddGlP7CEZjm2Rm3n7ocVqVkyLgO29CKwmgs2ryHAIujkx2Rd6pvDjJ85j/86tXVFZyhRtqyXpKktlIsTUZxszWZkJfZFTIYu8K8DCJfCG5mvLA/WMpk1adzlIVlSI8TexvVuTZiuLvgTcAuLsgR2KOvo4AAAPtklEQVSY/fIZNDTfJN3EqL5j2wxV3jybGLQspfUgZlu/Xn71Qtf3Xdd/0lAyx4Sa639z+R3n97LCdWjxRQ6nJeQw4DsgudZtPQ1IyFqghHQf9912U9D3XEwAWG8oQVakNBQuxt4caSOmgGhsYsVQc19WZqZYQgs368xMdSYLtYVp69cYtKpYT42h98UXXnmj4+9z334TXa6zAnj61BvO017M8/lMfb4EmLEJHmPaACDYPO1KT6B/3/Y8vZoh9XO1qUA50PSjND3j3Le764Kqflf5skzFt0Pmf2gfZdWXQXgknpmpqrGun+3nOqZC1Ios1jXVtarvfLy1VM+tjm2SpavXrGOir/2bwJf01JZMGFhNA/LQ/FkcmltMvXfp7PvgFmNqnqPP29OrCDQtKfr7++ODe/Af//k/wAN7t7U1ayUhOoIkioyQfRqYvTA9PS1PnTrV13uG5pAB4jVTtpxVyeuEfi9rbKe1mMUi5Bq91GrLoji4ywSVBVn206DGQuy9XX2qB5HYvtdLv5aEwGfv3e3MV2Yy588v1JwlwnzjpAgR0rGE9mnMOqhQ8zzWtBq7HkxWyti4bk3Qb6qTFfzN5Xeixpav35vF0Jc7yukoyiWB4x/bXSghwKa5FAB+cOxOAM3+PvTUotE/bLJSxuV6I0i7GdKXpnHgywW4cW0JV68tG2tn9rpnZY0Q4rSUctr3PWrCWiSdiQF0nVhsZO0ArcjK4TKWLE5rIddI8xwCWC2jdOxOPHlwT1RpJ52r167nJoBl3U+D0orG3tv1zLo2zHbNXgTrZSkx+/SqZkofg4pkxYJf+h/+HLNfdtdofWup7nzPIWMoub48NH82txJWIcTkfLvn1mpQ0ExSs3jxyrtd33Hlo4txZAea4ynk3at8czFjS5mhXaWmHvnoLdiycZ3x97aSPoMkxMIxM1W1qjiXLAIYELZ3+X4D+AMArlxbNmrgYkobFQ36hKEZMWMqG/L43bs6JHVb1vo8HKCB9OWOsiDU4djlH+C6hvpdLLZ35Ct2XilPYN2aUpc5KU35lBCyPoFl5Xyf971npqr47//srNVx2VdkvepwXg6hsSI7/M/Un4fmFo39nPQjMXGjRyPiyxln8sXRzdgxOeGyIqZPTb5aSZKVDWx5wu65tfl8tkCWe26tZuY8DzTLhjVWZPx4asldqj9svoCu9DyDdrdIEuo7GlM9RP+NTjInYchvgLh3liyFZWIQUdaxjL0mbH6h1iGAKZJS9PxCDT81FJxNOrOGEKPhSvpaFUm9HZvKwvS7WGzv+mcMmbR13mmsGNN85CGAqTQAWTIorWiae5dL9mXFV2R9/86t1kLooST72ZXvKoT9O7c6/WWU47eNkGCDfp/aY/rUtzEmf+cqdfTVM287tRahDt2hXG2sdAQfKJSG3kZjuSnM7zv2Eg7NLWLjujXGrPuuQ1DRIvNCLRy2sWHzD0xWWdDvd/Lw7UZrhW2sxb6zEG3o1KMvDkzjHMLYa8JcC7S++Bx9/pxxYdnoSQbn0hQNQsOVJbbF9NNPnQFgP9X3EgH34Nxiu4wGEF6frFdhy1WT7Np12aGFe/nVC95s+rEMesyYCqvb7u0KddeLFJ96/WJXSoJnTjcXyTUlkUnGdaB3jcQXW5qZ4x/fbfVXcd0j9P791JyEjCe1dvkc8JO/cz2HXj8yST+1FiH30ttq01baqgOkOZz3gxALh21sAN2Ru3rC7djr2dIdZVVtQZGsuqG3qQiMvRDmWjD0PFA2dbZrw/GFBBdpIKTB5dfjGuy9bjaqaGtvxT7iMAtgpY7owTQlh2IYxJhxFVa3YTNnJE/MJjNXvbHcszkqeWJ3ZQIPQc81Zcuv5dOIhGz6/dac+NwFXA75Lqdn3/NmEVQzCEy5En3mymHFNTbSHARD1662+4AlOKBXeq1OkAdjb450ZXsPKcTqWjiH2VkwBNezu57T9bvQ4s79FMBMlIQIKnw77KR5NpupMXlijhXGBZobnG+EPHxXZxWJLBZzZXJMYxYOcVIuQk6j0ALjk5Uy1pcncKhVNNtUEcFlTl6WMnUwzaBR+dH0556ZqmLx4Tvw2rE7u8yVo0Y/3GNmpqp44t5uE2Z5QrTnf3Wy4kyf4qJovnpjL4S5FsgH5xbxwSP/1nmqcy2cg4xo6we+zcX2nLaN7IG924bmhLwiZXsBGuV+TvNsJt+TJw7u6coJlEbzs/CZO/CDY3dao5UnK+WujcGlrY7hraV6qshh02+SuY4GGUoPdPt3uubhu9dXcOlqw+oHOjNVxfGP74btPKWe1+eX1W/0/vDlRwv1fyXpMM2Z4x/f3Z7/Jw/fjofvuiWVMF80X72xN0cm7dXryxOoN1baZhLXYmRa8HUGGdHWD3yRibbnNPkI7N+5te0PNAzozzbK/Zz22ULMD6ZoLV9bXL+tlEsdtVRDfJpi0P3Z0iTCLbJ2JMZPM6SkmcnnD1jV+OnvwxZ13k+U5cOVdypJEU1bo4RvzoREYCYpgsY5ydhrwoBOFeu162FLdnLBNzHIiLZ+MTNVxWfv3R39nEm19suvXsi8XE2oadN7nYRpJflso9zPeT6bOu1OVvxmhdJEp6OzTyPVSwSuiVHpTxu9am2Tv59fqOGZ07UOAez/b+9uQuw6ywCO/5+ZTCfTGNOGpl3EJP1K0Yg2i6FB66IbRYtgBaumIohgqKAbceFCcNNCV2q1FY1SLCpxI7ixUBC6qhoapdVGxK9SWlNqSj+MNmmTzOvi3huvZ+75uDP33vfMmf8Phszc8/GeM2/uuc+8H88bsCqLedms83VMjl2T4gzX4v+vMl1o7d7I6mZgtq3FeZRN3xJWVLfo7DgDEnPPaJuVSdznpB9mZYPm12L74ha2LW4pvbcu1/O0720wpq5uhuv2xdWzkOsGD49T57fesJOTp85cuo7LF+ZYXJjn1dfPd6o+y9QtMD5XM5i+2DJatu5tMQVFWTqLt25d4F/nzrOeSXKXL8xx7sJK43OMSlQ7qPOyDPBdaO3ugo38DM4ShEXEB4H7gXngByml+3JcB6xOITEXjHzTBpQuTbLWhKVdst77XO8MNugN2h7+0ITJLAj92tnzl5bbKdPlep7WvRWTJFepG9f1qe//msf/9vLY15B7aZO2qFtg/Lqv/KL2+GFNxxKW7Ve2PE6d4SV4oHrpqqIdFS2yTROdKp8mz6kmi4/P2sy7IyNiHngQ+BBwADgcEQdmfR0wOtlo2Tt/bi5GDsJca8JS/U9Zl8S4zp1f4RufOHgpWJ5Ud5R/7U7eV3/+B37cMACD6jpYawDW1u6JHOq6d5cWyj8qlhbmVv0OmyyRU7ffWoYTFAOpUff13ht2jjz2zBsXWrnwtiajrZ/VOVrCbgH+mlL6O0BE/BT4CPDHWV/IqFaSFUb/pXRxJY0chFk1hd83aDNVGbbHMfx7b9ICtjAXEIzMqD3gX7vTcez4c433rauDcQMwW79Gq2pJOHuhPCnM1hEz1Jq2HFXtd+LZl8fOGTcqbive12Bt4KKyZ3zZebSxtPWzOkcQthsYfgI/Dxwq7hQRR4AjAHv37p3KhZQ1hTddpLTqHA7YbG6Sv6vBuarOGfB/XZZlWdBh8mtAqqcuFUmxa3lSdTAqw7vqVVXXqyOGETQdo1O132DbsePPNU5dM+paiiaxuoE2nrZ+VucIwka1Ma96h6WUjgJHAZaXl6eSPKpqMGrTlAtdTk8wK2tZMLbqXFXnHF5oeKBsivPuK5b8sJ6Sqqzpo+poEqZ13s2gqr6qUtGsN5v6PXe861J+ubLB8U2upbjPOItKqxva+lmdI0XF88CeoZ/fBpzKcB2l0+8PH9rTeFp+l9MTzEqTjOJz0VuPrcrw732cerEOZ+/woT0jXy+momji1pIxPsOsz/WZZH2tVd1zomkdl2X0b+t6j5qMtj7nc7SEPQHsj4jrgH8AnwTuynAdlU3hy/t2NppFsZGnxrZFMelecUzeYC22wT7DCV4f+9Pp0lmpxf2tw/YYtG4Mz47cdtk89350/O7fn3zuPasG5++/ehuvv7lifU7IJOtrrYrv0x1LC0Qwdrf1YJ+urfeoam19zkfKsExMRNwOfJNeioqHUkr3Vu2/vLycTpw4MZNrkyRJWo+I+G1Kabluvyx5wlJKjwCP5ChbkiSpDVy2SJIkKQODMEmSpAwMwiRJkjIwCJMkScrAIEySJCkDgzBJkqQMDMIkSZIyMAiTJEnKwCBMkiQpgyzLFo0rIk4Dz065mKv6X8PeABYrjqnbPolzWIZlWMZ0z2EZlmEZm6uMl/pf07QvpbSrbqcsyxaNq8mNrFdEnAD2FF8GtlYdVrN9EuewDMuwjOmewzIswzI2VxkvNVnXcRbsjpQkScrAIEySJCmDDdEdOSNHgc8WXvsLsL/imLrtkziHZViGZUz3HJZhGZaxucp4qKasmdkQA/MlSZK6xu5ISZKkDFrbHRkRtwC/BLbnvhZJkqQxfSal9HDVDq3tjoyIm4GPA9cAS/3vWxs0SpIkAW8CC8CLwIGU0itlO7Y2CCuKiBfoJVM1EJMkSTkkejnHqrb9GbgROA58O6V0rOxkGyKgiYj3AbuA+dzXIkmSNq2yAGx42439718DdledrPUD8yPiGuBRDMAkSVK7VHUnXlGzvd1BWEQsASfpjQlL1NyMJEnSDI1qGTvb//dK4FTVwa0NwiIigKfp3YQkSVJblDUKJeAyesHZlfR68kq1NggD7gaup3eNMfQlSZKUU1k8EvRmRkJvLPvdlSfZKLMjJUmSuqTNLWGSJEmdZRAmSZKUgUGYJElSBgZhkiRJGRiESZIkZWAQJqlTIuJiRDwZEScj4qmI+FJEVD7rIuLaiLhrVtcoSWAQJql7zqaUDqaU3gm8H7gd+FrNMdcCBmGSZso8YZI6JSL+nVJ6y9DP1wNPAFcB+4AfAdv6m7+QUvpVRPwGeAfwDPAw8C3gPuA2YBF4MKX0vZndhKRNwSBMUqcUg7D+a68AbwfOACsppXMRsR84llJajojbgC+nlD7c3/8IcHVK6Z6IWAQeB+5MKT0z05uR1Glbcl+AJM3AYImRBeCBiDgIXARuKtn/A8C7I+Jj/Z93APvptZRJ0kQYhEnqtH535EXgn/TGhr0I3ExvTOy5ssOAL6aUKhfflaT1cGC+pM6KiF3Ad4EHUm/sxQ7ghZTSCvBpYL6/6xlg+9ChjwKfj4iF/nluiohtSNIE2RImqWuWIuJJel2PF+gNxP96f9t3gJ9FxJ3AY8B/+q//HrgQEU8BPwTupzdj8ncREcBp4I5Z3YCkzcGB+ZIkSRnYHSlJkpSBQZgkSVIGBmGSJEkZGIRJkiRlYBAmSZKUgUGYJElSBgZhkiRJGRiESZIkZfBf7ARSQoGm3jcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a scatter plot\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.scatter(train['date'], train['wind_speed'])\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Wind Speed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:46:47.351892Z",
     "iopub.status.busy": "2024-07-02T15:46:47.351367Z",
     "iopub.status.idle": "2024-07-02T15:47:01.260279Z",
     "shell.execute_reply": "2024-07-02T15:47:01.259062Z",
     "shell.execute_reply.started": "2024-07-02T15:46:47.351838Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vqncAm4C1TbO1wB3N9iZgTZLjk5xJdxHCfc106tNJLmhWk17Wc4wkSdKCMeUVt6p6Nsm7qmoj8H9m6JzXABuTXA48BlzanGtbko3Ag8B+4MqqOtAccwVwEzAK3Nm8JEmSFpR0F2pO0SD593Rvvvtx4JsH68P+BIVOp1Pj4+NzPQxJkqRpJdlSVdPebq2f37j98+b9yp5aAa96IQOTJEnSCzNtcKuqM2djIJIkSZraERcnJHlDki8l+UaSe5L84GwOTJIkSc831arSDwO/DHwv8EG6N9OVJEnSHJkquL2oqu5uHkH1CcA72kqSJM2hqX7jdlKSS460X1W3DW5YkiRJOtRUwe2zwFuPsF+AwU2SJGkWHTG4VdUvzOZAJEmSNLV+HjIvSZKkIWBwkyRJagmDmyRJUkv088grkvwwsKy3fVXdPKAxSZIkaRLTBrckfwC8GngAONCUCzC4SZIkzaJ+rrh1gLOrqgY9GEmSJB1ZP79x+zLwfYMeiCRJkqbWzxW3VwAPJrkPeOZgsareNrBRSZIk6TD9BLdfH/QgJEmSNL1pg1tVfXY2BiJJkqSpTfsbtyQXJPlCkm8k+U6SA0m+PhuDkyRJ0nP6WZzwO8DbgUeAUeBfNDVJkiTNor5uwFtVO5KMVNUB4KNJ/nrA45IkSdIh+glu30ryYuCBJB8AdgMnDHZYkiRJOlQ/U6XvbNq9C/gmsBT46UEOSpIkSYfrZ1XpV5OMAqdX1W/MwpgkSZI0iX5Wlb6V7nNKP9Xsn5tk06AHJkmSpOfrZ6r014EVwB6AqnoAWDa4IUmSJGky/QS3/VX1/wY+EkmSJE2pn1WlX07yc8BIkuXAewBvByJJkjTL+rni9m7gHLoPmL8F+Drwi4MclCRJkg43bXCrqm9V1b+rqn9cVZ1m+9vTHZfkJUnuS/KlJNuS/EZTPyXJ3Ukead5P7jlmfZIdSbYnWdlTPz/J1uaz65LkhX5hSZKktjriVOl0K0er6m3T9P0M8GNV9Y0ki4C/SnIncAmwuaquSXI1cDXwviRnA2voXt07A/izJK9pntZwA7AO+Dzwp8Aq4M6+vqEkSdI8MdVv3C4EHqc7PXovcFRXuaqqgG80u4uaVwEXA29s6huAzwDva+q3VtUzwKNJdgArknwFOLGq7gFIcjOwGoObJElaYKaaKv0+4FeB1wIfAt4M/H1VfbaqPttP50lGkjwAPAXcXVX3AqdV1W6A5v3UpvliukHxoJ1NbXGzfWhdkiRpQTlicKuqA1X1qapaC1wA7AA+k+Td/Xbe9HEusITu1bPXTtF8sit6NUX98A6SdUnGk4xPTEz0O0xJkqRWmHJxQpLjk1wC/CFwJXAdcNvRnqSq9tCdEl0FPJnk9Kb/0+lejYPulbSlPYctAXY19SWT1Cc7z43NAorO2NjY0Q5TkiRpqB0xuCXZQPd+ba8HfqNZVfqfquqJfjpOMpbkpGZ7FPhx4GFgE7C2abYWuKPZ3gSsacLimcBy4L5mOvXpJBc0q0kv6zlGkiRpwZhqccI7gW8CrwHe03MHjtBde3DiNH2fDmxIMkI3IG6sqj9Jcg+wMcnlwGPApXQ73JZkI/AgsB+4sllRCnAFcBMwSndRggsTJEnSgpPu4s/5p9Pp1Pj4+FwPQ5IkaVpJtlRVZ7p2/Tw5QZIkSUPA4CZJktQSBjdJkqSWMLhJkiS1hMFNkiSpJQxukiRJLWFwkyRJagmDmyRJUksY3CRJklrC4CZJktQSBjdJkqSWMLhJkiS1hMFNkiSpJQxukiRJLWFwkyRJagmDmyRJUksY3CRJklrC4CZJktQSBjdJkqSWMLhJkiS1hMFNkiSpJQxukiRJLWFwkyRJagmDmyRJUksY3CRJklrC4CZJktQSBjdJkqSWMLhJkiS1hMFNkiSpJQxukiRJLTGw4JZkaZK/SPJQkm1J3tvUT0lyd5JHmveTe45Zn2RHku1JVvbUz0+ytfnsuiQZ1LglSZKG1SCvuO0H/m1V/SBwAXBlkrOBq4HNVbUc2Nzs03y2BjgHWAVcn2Sk6esGYB2wvHmtGuC4JUmShtLAgltV7a6qLzbbTwMPAYuBi4ENTbMNwOpm+2Lg1qp6pqoeBXYAK5KcDpxYVfdUVQE39xwjSZK0YMzKb9ySLAPOA+4FTquq3dANd8CpTbPFwOM9h+1saoub7UPrkiRJC8rAg1uSlwF/DPxiVX19qqaT1GqK+mTnWpdkPMn4xMTE0Q9WkiRpiA00uCVZRDe0fayqbmvKTzbTnzTvTzX1ncDSnsOXALua+pJJ6oepqhurqlNVnbGxsZn7IpIkSUNgkKtKA/we8FBVfbDno03A2mZ7LXBHT31NkuOTnEl3EcJ9zXTq00kuaPq8rOcYSZKkBeO4AfZ9EfBOYGuSB5rarwLXABuTXA48BlwKUFXbkmwEHqS7IvXKqjrQHHcFcBMwCtzZvCRJkhaUdBdqzj+dTqfGx8fnehiSJEnTSrKlqjrTtfPJCZIkSS1hcJMkSWoJg5skSVJLGNwkSZJawuAmSZLUEgY3SZKkljC4SZIktYTBTZIkqSUMbpIkSS1hcJMkSWoJg5skSVJLGNwkSZJawuAmSZLUEgY3SZKkljC4SZIktYTBTZIkqSUMbpIkSS1x3FwPQJIk6aDb73+Ca+/azq49eznjpFGuWnkWq89bPOd9DQuDmyRJGgq33/8E62/byt59BwB4Ys9e1t+2FeCoA9dM9jVMnCqVJElD4dq7tn83aB20d98Brr1r+5z2NUwMbpIkaSjs2rP3qOqz1dcwMbhJkqShcMZJo0dVn62+honBTZIkDYWrVp7F6KKR59VGF41w1cqz5rSvYeLiBEmSNBQOLhqYiZWgM9nXMElVzfUYBqLT6dT4+PhcD0OSJGlaSbZUVWe6dk6VSpIktYTBTZIkqSUMbpIkSS1hcJMkSWqJga0qTfL7wFuAp6rqtU3tFODjwDLgK8DPVtU/NJ+tBy4HDgDvqaq7mvr5wE3AKPCnwHtrSFZUHPoMtB/9gTE+ft9j7Hv2+e0uevUpfOxfXjg3g9Qx+/nfvYfP/e3XDqu/KHDhq05h266n2bN332GfjwROHF3Enm/tmzermRaC7mNy/oa9h/5DBk548Qg/9frF/MXDE9/9d7/se0cn/ftxqJGEt79hKb+5+nWTnnO+rXxrsyP9m4fu34Hf+qnXsfq8xdx+/xP8+qZt3/33f/JLF/H+t57zvD87/2w10wa2qjTJjwDfAG7uCW4fAL5WVdckuRo4uarel+Rs4BZgBXAG8GfAa6rqQJL7gPcCn6cb3K6rqjunO/8gV5X+2u1b+cPPPzaQviVJ0vAZ9EWYOV9VWlV/CRz6f1kuBjY02xuA1T31W6vqmap6FNgBrEhyOnBiVd3TXGW7ueeYOWFokyRp4fnc336Nn//de+Z6GLP+G7fTqmo3QPN+alNfDDze025nU1vcbB9anzO33Pv49I0kSdK808/PIgZtWBYnZJJaTVGfvJNkXZLxJOMTExMzNrheB4bj53WSJGkBmu3g9mQz/Unz/lRT3wks7Wm3BNjV1JdMUp9UVd1YVZ2q6oyNjc3owA8ayWRZUpIkafBmO7htAtY222uBO3rqa5Icn+RMYDlwXzOd+nSSC5IEuKznmDnx9jcsnb6RJEmady569SlzPYSB3g7kFuCNwCuS7ATeD1wDbExyOfAYcClAVW1LshF4ENgPXFlVB5quruC524Hc2bzmzMGl/C5QkHSoMMVvOdRKI4ED/qGK4bm1lw+Z10B5D6P2+LXbt3LLvY9zoIoAi0bCd5r/xZrs/lRauPx3PT9cdM2f88SevYfVF580yueu/rEpj+3e73Are/cd+G5tdNEIv33J6/y78AL1ezsQg5skSQvQsYYvA/zM6je4DWyqVJIkDa+DIeuFhq/V5y02qM0Bg5skSQuU4at9huU+bpIkSZqGwU2SJKklDG6SJEkt4W/cJEnSjHPV6WAY3CRJ0ow69FYjT+zZy/rbtgIY3o6RU6WSJGlGXXvX9ufdHw5g774DXHvX9jka0fxhcJMkSTNq1yRPZJiqrv4Z3CRJ0ow646TRo6qrfwY3SZI0o65aeRaji0aeVxtdNMJVK8+aoxHNHy5OkCRJM+pYH6elIzO4SZKkGefjtAbDqVJJkqSWMLhJkiS1hMFNkiSpJQxukiRJLWFwkyRJaglXlUqSpGPmQ+Vnh8FNkiQdEx8qP3ucKpUkScfEh8rPHoObJEk6Jj5UfvYY3CRJ0jHxofKzx+AmSZKOiQ+Vnz0uTpAkScfEh8rPHoObJEk6Zj5UfnY4VSpJktQSBjdJkqSWMLhJkiS1RGt+45ZkFfAhYAT4SFVdM8dDkiRJLTIfHsvViuCWZAT4MPBmYCfwhSSbqurBuR2ZJEmazLCFpPnyWK62TJWuAHZU1d9V1XeAW4GL53hMkiRpEgdD0hN79lI8F5Juv/+JORvTfHksV1uC22Lg8Z79nU1NkiQNmWEMSfPlsVxtCW6ZpFaHNUrWJRlPMj4xMTELw5IkSYcaxpA0Xx7L1ZbgthNY2rO/BNh1aKOqurGqOlXVGRsbm7XBSZKk5wxjSJovj+VqS3D7ArA8yZlJXgysATbN8ZgkSdIkhjEkrT5vMb99yetYfNIoARafNMpvX/K6Vi1MgJasKq2q/UneBdxF93Ygv19V2+Z4WJIkaRLD+uzS+fBYrlQd9lOxeaHT6dT4+PhcD0OSJGlaSbZUVWe6dm2ZKpUkSVrwDG6SJEktYXCTJElqCYObJElSSxjcJEmSWsLgJkmS1BIGN0mSpJYwuEmSJLWEwU2SJKklDG6SJEktMW8feZVkAvjqgE9zJnB8s/1Mz/ZkjvVzz+E5FvI5ZqIPz+E5PIfneKF9PDxNnzPh+6tqbLpGrXjI/AvRz5c/Vkm+Cbzk4G7P9qTNj/Fzz+E5FvI5ZqIPz+E5PIfneEF99PMM0dniVKkkSVJLGNwkSZJaYt5Olc6S24DlzfYjPduTOdbPPYfnWMjnmIk+PIfn8ByeY6b6mDPzdnGCJEnSfONUqSRJUkvMq6nSJG8BPsk8+16SJGneexY4rqaZCp1vAWcvsKHZXgRcNodjkSRJC1fRvbXIZPbRzSnQvW8cdO8ddymwcapO5/Vv3JJ8m244HZnrsUiSpAVpqgAHsJ/uDX5fC/xBVU150Wm+XXH7riQ/w/R3T5YkSRqkqUIbdLPY2c32SdN1Ni8XJyQ5E/j4XI9DkiRpCs8esn/ydAfMu+CW5OXAdroJt4ADczsiSZKkSb2IblbZ2+zv7+eAeSNJgF08NwX8LNNfopQkSZor3wJe3Gx/ZLrG8yq4AdcCL6Mb1kJ3UcJ8+46SJGn+OIHnVphuSPKrUzWe16tKJUmS5hOvRkmSJLWEwU2SJKklDG6SJEktYXCTJElqCYObJElSSxjcJC14SQ4keSDJtiRfSvJLSab872OSZUl+brbGKElgcJMkgL1VdW5VnQO8GfhJ4P3THLMMMLhJmlXex03SgpfkG1X1sp79VwFfAF4BfD/wB3Rvkgnwrqr66ySfB34QeBTYAFwHXAO8ETge+HBV/c9Z+xKSFgSDm6QF79Dg1tT+AfgB4Gng2ar6dpLlwC1V1UnyRuCXq+otTft1wKlV9ZtJjgc+B1xaVY/O6peRNK8dN30TSVqQDj7neBHwO0nOBQ4ArzlC+58A/lGSn2n2vwdYTveKnCTNCIObJB2imSo9ADxF97duTwI/RPd3wd8+0mHAu6vqrlkZpKQFycUJktQjyRjwP4Dfqe5vSb4H2F1VzwLvBEaapk8DL+859C7giiSLmn5ek+QEJGkGecVNkmA0yQN0p0X3012M8MHms+uBP05yKfAXwDeb+t8A+5N8CbgJ+BDdlaZfTBJgAlg9W19A0sLg4gRJkqSWcKpUkiSpJQxukiRJLWFwkyRJagmDmyRJUksY3CRJklrC4CZJktQSBjdJkqSWMLhJkiS1xP8H5nZ7FfaqygUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting a scatter plot\n",
    "fig, ax = plt.subplots(figsize=(10,6))\n",
    "ax.scatter(train['date'], train['meanpressure'])\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Mean Pressure')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicting Mean Temperature of Delhi**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:47:01.262166Z",
     "iopub.status.busy": "2024-07-02T15:47:01.261787Z",
     "iopub.status.idle": "2024-07-02T15:47:01.268254Z",
     "shell.execute_reply": "2024-07-02T15:47:01.266925Z",
     "shell.execute_reply.started": "2024-07-02T15:47:01.262129Z"
    }
   },
   "outputs": [],
   "source": [
    "## MEAN TEMP \n",
    "temp_train = train.iloc[:,1:2]\n",
    "temp_test = test.iloc[:,1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:47:01.270519Z",
     "iopub.status.busy": "2024-07-02T15:47:01.270040Z",
     "iopub.status.idle": "2024-07-02T15:47:01.293489Z",
     "shell.execute_reply": "2024-07-02T15:47:01.292251Z",
     "shell.execute_reply.started": "2024-07-02T15:47:01.270470Z"
    }
   },
   "outputs": [],
   "source": [
    "#Scaling the values between 0 to 1\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "ss= MinMaxScaler(feature_range=(0,1))\n",
    "temp_train= ss.fit_transform(temp_train)\n",
    "temp_test= ss.fit_transform(temp_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:47:01.295835Z",
     "iopub.status.busy": "2024-07-02T15:47:01.295327Z",
     "iopub.status.idle": "2024-07-02T15:47:01.307897Z",
     "shell.execute_reply": "2024-07-02T15:47:01.306348Z",
     "shell.execute_reply.started": "2024-07-02T15:47:01.295775Z"
    }
   },
   "outputs": [],
   "source": [
    "def create_dataset(dataset, look_back=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-look_back-1):\n",
    "        a = dataset[i:(i+look_back), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + look_back, 0])\n",
    "    return np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:47:01.310048Z",
     "iopub.status.busy": "2024-07-02T15:47:01.309626Z",
     "iopub.status.idle": "2024-07-02T15:47:01.327532Z",
     "shell.execute_reply": "2024-07-02T15:47:01.326590Z",
     "shell.execute_reply.started": "2024-07-02T15:47:01.310010Z"
    }
   },
   "outputs": [],
   "source": [
    "look_back = 1\n",
    "trainX, trainY = create_dataset(temp_train, look_back)\n",
    "testX, testY = create_dataset(temp_test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:47:01.330013Z",
     "iopub.status.busy": "2024-07-02T15:47:01.329479Z",
     "iopub.status.idle": "2024-07-02T15:47:01.339279Z",
     "shell.execute_reply": "2024-07-02T15:47:01.338041Z",
     "shell.execute_reply.started": "2024-07-02T15:47:01.329952Z"
    }
   },
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1],1))\n",
    "testX = np.reshape(testX, (testX.shape[0], testX.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:47:01.341676Z",
     "iopub.status.busy": "2024-07-02T15:47:01.341191Z",
     "iopub.status.idle": "2024-07-02T15:47:47.971530Z",
     "shell.execute_reply": "2024-07-02T15:47:47.970356Z",
     "shell.execute_reply.started": "2024-07-02T15:47:01.341626Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.1964 - accuracy: 6.8493e-04\n",
      "Epoch 2/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0249 - accuracy: 0.0014\n",
      "Epoch 3/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0136 - accuracy: 0.0014\n",
      "Epoch 4/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0087 - accuracy: 0.0014\n",
      "Epoch 5/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0091 - accuracy: 0.0014\n",
      "Epoch 6/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0085 - accuracy: 0.0014\n",
      "Epoch 7/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0081 - accuracy: 0.0014\n",
      "Epoch 8/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0075 - accuracy: 0.0014\n",
      "Epoch 9/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0074 - accuracy: 0.0014\n",
      "Epoch 10/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0067 - accuracy: 0.0014\n",
      "Epoch 11/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0065 - accuracy: 0.0014\n",
      "Epoch 12/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0063 - accuracy: 0.0014\n",
      "Epoch 13/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0059 - accuracy: 0.0014\n",
      "Epoch 14/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0060 - accuracy: 0.0014\n",
      "Epoch 15/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0056 - accuracy: 0.0014\n",
      "Epoch 16/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0053 - accuracy: 0.0014\n",
      "Epoch 17/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0060 - accuracy: 0.0014\n",
      "Epoch 18/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0055 - accuracy: 0.0014\n",
      "Epoch 19/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0054 - accuracy: 0.0014\n",
      "Epoch 20/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0056 - accuracy: 0.0014\n",
      "Epoch 21/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0054 - accuracy: 0.0014\n",
      "Epoch 22/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0052 - accuracy: 0.0014\n",
      "Epoch 23/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0050 - accuracy: 0.0014\n",
      "Epoch 24/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0050 - accuracy: 0.0014\n",
      "Epoch 25/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0051 - accuracy: 0.0014\n",
      "Epoch 26/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0050 - accuracy: 0.0014\n",
      "Epoch 27/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0051 - accuracy: 0.0014\n",
      "Epoch 28/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0051 - accuracy: 0.0014\n",
      "Epoch 29/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0048 - accuracy: 0.0014\n",
      "Epoch 30/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0047 - accuracy: 0.0014\n",
      "Epoch 31/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0047 - accuracy: 0.0014\n",
      "Epoch 32/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0051 - accuracy: 0.0014\n",
      "Epoch 33/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0050 - accuracy: 0.0014\n",
      "Epoch 34/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0047 - accuracy: 0.0014\n",
      "Epoch 35/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0046 - accuracy: 0.0014\n",
      "Epoch 36/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0048 - accuracy: 0.0014\n",
      "Epoch 37/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0045 - accuracy: 0.0014\n",
      "Epoch 38/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0047 - accuracy: 0.0014\n",
      "Epoch 39/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0045 - accuracy: 0.0014\n",
      "Epoch 40/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0045 - accuracy: 0.0014\n",
      "Epoch 41/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0047 - accuracy: 0.0014\n",
      "Epoch 42/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0042 - accuracy: 0.0014\n",
      "Epoch 43/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0045 - accuracy: 0.0014\n",
      "Epoch 44/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0047 - accuracy: 0.0014\n",
      "Epoch 45/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0043 - accuracy: 0.0014\n",
      "Epoch 46/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0046 - accuracy: 0.0014\n",
      "Epoch 47/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0043 - accuracy: 0.0014\n",
      "Epoch 48/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0042 - accuracy: 0.0014\n",
      "Epoch 49/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0043 - accuracy: 0.0014\n",
      "Epoch 50/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0042 - accuracy: 0.0014\n",
      "Epoch 51/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0041 - accuracy: 0.0014\n",
      "Epoch 52/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0041 - accuracy: 0.0014\n",
      "Epoch 53/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0040 - accuracy: 0.0014\n",
      "Epoch 54/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0042 - accuracy: 0.0014\n",
      "Epoch 55/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0042 - accuracy: 0.0014\n",
      "Epoch 56/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0044 - accuracy: 0.0014\n",
      "Epoch 57/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0043 - accuracy: 0.0014\n",
      "Epoch 58/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0040 - accuracy: 0.0014\n",
      "Epoch 59/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0040 - accuracy: 0.0014\n",
      "Epoch 60/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0039 - accuracy: 0.0014\n",
      "Epoch 61/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0042 - accuracy: 0.0014\n",
      "Epoch 62/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0039 - accuracy: 0.0014\n",
      "Epoch 63/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0039 - accuracy: 0.0014\n",
      "Epoch 64/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0041 - accuracy: 0.0014\n",
      "Epoch 65/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0041 - accuracy: 0.0014\n",
      "Epoch 66/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0042 - accuracy: 0.0014\n",
      "Epoch 67/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0040 - accuracy: 0.0014\n",
      "Epoch 68/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0039 - accuracy: 0.0014\n",
      "Epoch 69/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0039 - accuracy: 0.0014\n",
      "Epoch 70/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0037 - accuracy: 0.0014\n",
      "Epoch 71/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0041 - accuracy: 0.0014\n",
      "Epoch 72/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0040 - accuracy: 0.0014\n",
      "Epoch 73/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0038 - accuracy: 0.0014\n",
      "Epoch 74/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0039 - accuracy: 0.0014\n",
      "Epoch 75/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0037 - accuracy: 0.0014\n",
      "Epoch 76/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0038 - accuracy: 0.0014\n",
      "Epoch 77/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0037 - accuracy: 0.0014\n",
      "Epoch 78/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0039 - accuracy: 0.0014\n",
      "Epoch 79/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0039 - accuracy: 0.0014\n",
      "Epoch 80/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0039 - accuracy: 0.0014\n",
      "Epoch 81/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0038 - accuracy: 0.0014\n",
      "Epoch 82/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0038 - accuracy: 0.0014\n",
      "Epoch 83/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0037 - accuracy: 0.0014\n",
      "Epoch 84/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0037 - accuracy: 0.0014\n",
      "Epoch 85/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0037 - accuracy: 0.0014\n",
      "Epoch 86/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0037 - accuracy: 0.0014\n",
      "Epoch 87/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0036 - accuracy: 0.0014\n",
      "Epoch 88/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0037 - accuracy: 0.0014\n",
      "Epoch 89/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0037 - accuracy: 0.0014\n",
      "Epoch 90/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0034 - accuracy: 0.0014\n",
      "Epoch 91/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0035 - accuracy: 0.0014\n",
      "Epoch 92/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0034 - accuracy: 0.0014\n",
      "Epoch 93/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0036 - accuracy: 0.0014\n",
      "Epoch 94/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0038 - accuracy: 0.0014\n",
      "Epoch 95/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0036 - accuracy: 0.0014\n",
      "Epoch 96/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0036 - accuracy: 0.0014\n",
      "Epoch 97/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0035 - accuracy: 0.0014\n",
      "Epoch 98/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0035 - accuracy: 0.0014\n",
      "Epoch 99/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0036 - accuracy: 0.0014\n",
      "Epoch 100/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0037 - accuracy: 0.0014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7a0cd0519410>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create and fit the LSTM network\n",
    "model_temp = Sequential()\n",
    "#Adding the first LSTM layer and some Dropout regularisation\n",
    "model_temp.add(LSTM(units = 100, return_sequences = True, input_shape = (trainX.shape[1], 1)))\n",
    "model_temp.add(Dropout(0.2))\n",
    "# Adding a second LSTM layer and some Dropout regularisation\n",
    "model_temp.add(LSTM(units = 100, return_sequences = True))\n",
    "model_temp.add(Dropout(0.2))\n",
    "# Adding a third LSTM layer and some Dropout regularisation\n",
    "model_temp.add(LSTM(units = 100, return_sequences = True))\n",
    "model_temp.add(Dropout(0.2))\n",
    "# Adding a fourth LSTM layer and some Dropout regularisation\n",
    "model_temp.add(LSTM(units = 50))\n",
    "model_temp.add(Dropout(0.2))\n",
    "# Adding the output layer\n",
    "model_temp.add(Dense(units = 1))\n",
    "\n",
    "# Compiling the RNN\n",
    "model_temp.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics=['accuracy'])\n",
    "\n",
    "# Fitting the RNN to the Training set\n",
    "model_temp.fit(trainX, trainY, epochs = 100, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:47:47.974087Z",
     "iopub.status.busy": "2024-07-02T15:47:47.973553Z",
     "iopub.status.idle": "2024-07-02T15:47:49.537916Z",
     "shell.execute_reply": "2024-07-02T15:47:49.536617Z",
     "shell.execute_reply.started": "2024-07-02T15:47:47.974035Z"
    }
   },
   "outputs": [],
   "source": [
    "# PREDICTION\n",
    "\n",
    "prediction = model_temp.predict(testX)\n",
    "prediction = ss.inverse_transform(prediction)\n",
    "temp_test = ss.inverse_transform(temp_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:47:49.540060Z",
     "iopub.status.busy": "2024-07-02T15:47:49.539603Z",
     "iopub.status.idle": "2024-07-02T15:47:49.808836Z",
     "shell.execute_reply": "2024-07-02T15:47:49.807708Z",
     "shell.execute_reply.started": "2024-07-02T15:47:49.540018Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(temp_test, color = 'black', label = 'Delhi Mean Temperature')\n",
    "plt.plot(prediction, color = 'green', label = 'Predicted Delhi Mean Temperature')\n",
    "plt.title('Delhi Mean Temp Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Mean Temp')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicting Humidity of Delhi**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:47:49.810937Z",
     "iopub.status.busy": "2024-07-02T15:47:49.810461Z",
     "iopub.status.idle": "2024-07-02T15:47:49.818374Z",
     "shell.execute_reply": "2024-07-02T15:47:49.817033Z",
     "shell.execute_reply.started": "2024-07-02T15:47:49.810888Z"
    }
   },
   "outputs": [],
   "source": [
    "# Humidity\n",
    "\n",
    "humid_train = train.iloc[:,2:3]\n",
    "humid_test = test.iloc[:,2:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:47:49.820210Z",
     "iopub.status.busy": "2024-07-02T15:47:49.819843Z",
     "iopub.status.idle": "2024-07-02T15:47:49.842577Z",
     "shell.execute_reply": "2024-07-02T15:47:49.841443Z",
     "shell.execute_reply.started": "2024-07-02T15:47:49.820173Z"
    }
   },
   "outputs": [],
   "source": [
    "#Scaling the values between 0 to 1\n",
    "\n",
    "ss= MinMaxScaler(feature_range=(0,1))\n",
    "humid_train= ss.fit_transform(humid_train)\n",
    "humid_test= ss.fit_transform(humid_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:47:49.844944Z",
     "iopub.status.busy": "2024-07-02T15:47:49.844456Z",
     "iopub.status.idle": "2024-07-02T15:47:49.862038Z",
     "shell.execute_reply": "2024-07-02T15:47:49.861052Z",
     "shell.execute_reply.started": "2024-07-02T15:47:49.844896Z"
    }
   },
   "outputs": [],
   "source": [
    "look_back = 1\n",
    "trainX, trainY = create_dataset(humid_train, look_back)\n",
    "testX, testY = create_dataset(humid_test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:47:49.863761Z",
     "iopub.status.busy": "2024-07-02T15:47:49.863368Z",
     "iopub.status.idle": "2024-07-02T15:47:49.874408Z",
     "shell.execute_reply": "2024-07-02T15:47:49.873243Z",
     "shell.execute_reply.started": "2024-07-02T15:47:49.863688Z"
    }
   },
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1],1))\n",
    "testX = np.reshape(testX, (testX.shape[0], testX.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:47:49.876234Z",
     "iopub.status.busy": "2024-07-02T15:47:49.875892Z",
     "iopub.status.idle": "2024-07-02T15:48:35.759551Z",
     "shell.execute_reply": "2024-07-02T15:48:35.758164Z",
     "shell.execute_reply.started": "2024-07-02T15:47:49.876202Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.1594 - accuracy: 6.8493e-04\n",
      "Epoch 2/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0241 - accuracy: 0.0000e+00\n",
      "Epoch 3/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0180 - accuracy: 6.8493e-04\n",
      "Epoch 4/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0145 - accuracy: 6.8493e-04\n",
      "Epoch 5/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0138 - accuracy: 6.8493e-04\n",
      "Epoch 6/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0127 - accuracy: 6.8493e-04\n",
      "Epoch 7/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0127 - accuracy: 6.8493e-04\n",
      "Epoch 8/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0123 - accuracy: 6.8493e-04\n",
      "Epoch 9/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0125 - accuracy: 6.8493e-04\n",
      "Epoch 10/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0119 - accuracy: 6.8493e-04\n",
      "Epoch 11/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0114 - accuracy: 6.8493e-04\n",
      "Epoch 12/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0116 - accuracy: 6.8493e-04\n",
      "Epoch 13/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0109 - accuracy: 6.8493e-04\n",
      "Epoch 14/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0110 - accuracy: 6.8493e-04\n",
      "Epoch 15/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0110 - accuracy: 6.8493e-04\n",
      "Epoch 16/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0105 - accuracy: 6.8493e-04\n",
      "Epoch 17/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0107 - accuracy: 6.8493e-04\n",
      "Epoch 18/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0100 - accuracy: 6.8493e-04\n",
      "Epoch 19/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0105 - accuracy: 6.8493e-04\n",
      "Epoch 20/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0109 - accuracy: 6.8493e-04\n",
      "Epoch 21/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0101 - accuracy: 6.8493e-04\n",
      "Epoch 22/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0106 - accuracy: 6.8493e-04\n",
      "Epoch 23/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0108 - accuracy: 6.8493e-04\n",
      "Epoch 24/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0105 - accuracy: 6.8493e-04\n",
      "Epoch 25/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0105 - accuracy: 6.8493e-04\n",
      "Epoch 26/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0100 - accuracy: 6.8493e-04\n",
      "Epoch 27/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0107 - accuracy: 6.8493e-04\n",
      "Epoch 28/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0102 - accuracy: 6.8493e-04\n",
      "Epoch 29/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0099 - accuracy: 6.8493e-04\n",
      "Epoch 30/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0101 - accuracy: 6.8493e-04\n",
      "Epoch 31/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0102 - accuracy: 6.8493e-04\n",
      "Epoch 32/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0104 - accuracy: 6.8493e-04\n",
      "Epoch 33/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0104 - accuracy: 6.8493e-04\n",
      "Epoch 34/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0103 - accuracy: 6.8493e-04\n",
      "Epoch 35/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0099 - accuracy: 6.8493e-04\n",
      "Epoch 36/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0101 - accuracy: 6.8493e-04\n",
      "Epoch 37/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0099 - accuracy: 6.8493e-04\n",
      "Epoch 38/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0104 - accuracy: 6.8493e-04\n",
      "Epoch 39/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0103 - accuracy: 6.8493e-04\n",
      "Epoch 40/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0100 - accuracy: 6.8493e-04\n",
      "Epoch 41/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0100 - accuracy: 6.8493e-04\n",
      "Epoch 42/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0102 - accuracy: 6.8493e-04\n",
      "Epoch 43/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0097 - accuracy: 6.8493e-04\n",
      "Epoch 44/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0097 - accuracy: 6.8493e-04\n",
      "Epoch 45/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0103 - accuracy: 6.8493e-04\n",
      "Epoch 46/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0097 - accuracy: 6.8493e-04\n",
      "Epoch 47/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0096 - accuracy: 6.8493e-04\n",
      "Epoch 48/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0102 - accuracy: 6.8493e-04\n",
      "Epoch 49/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0096 - accuracy: 6.8493e-04\n",
      "Epoch 50/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0102 - accuracy: 6.8493e-04\n",
      "Epoch 51/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0096 - accuracy: 6.8493e-04\n",
      "Epoch 52/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0097 - accuracy: 6.8493e-04\n",
      "Epoch 53/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0095 - accuracy: 6.8493e-04\n",
      "Epoch 54/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 6.8493e-04\n",
      "Epoch 55/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0101 - accuracy: 6.8493e-04\n",
      "Epoch 56/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n",
      "Epoch 57/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0095 - accuracy: 6.8493e-04\n",
      "Epoch 58/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0096 - accuracy: 6.8493e-04\n",
      "Epoch 59/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 6.8493e-04\n",
      "Epoch 60/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0097 - accuracy: 6.8493e-04\n",
      "Epoch 61/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n",
      "Epoch 62/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0097 - accuracy: 6.8493e-04\n",
      "Epoch 63/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0097 - accuracy: 6.8493e-04\n",
      "Epoch 64/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0098 - accuracy: 6.8493e-04\n",
      "Epoch 65/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n",
      "Epoch 66/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0096 - accuracy: 6.8493e-04\n",
      "Epoch 67/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0096 - accuracy: 6.8493e-04\n",
      "Epoch 68/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0095 - accuracy: 6.8493e-04\n",
      "Epoch 69/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0095 - accuracy: 6.8493e-04\n",
      "Epoch 70/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0096 - accuracy: 6.8493e-04\n",
      "Epoch 71/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0096 - accuracy: 6.8493e-04\n",
      "Epoch 72/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n",
      "Epoch 73/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 6.8493e-04\n",
      "Epoch 74/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0095 - accuracy: 6.8493e-04\n",
      "Epoch 75/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n",
      "Epoch 76/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n",
      "Epoch 77/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0097 - accuracy: 6.8493e-04\n",
      "Epoch 78/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0090 - accuracy: 6.8493e-04\n",
      "Epoch 79/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0095 - accuracy: 6.8493e-04\n",
      "Epoch 80/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0097 - accuracy: 6.8493e-04\n",
      "Epoch 81/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n",
      "Epoch 82/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 6.8493e-04\n",
      "Epoch 83/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 6.8493e-04\n",
      "Epoch 84/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 6.8493e-04\n",
      "Epoch 85/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n",
      "Epoch 86/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 6.8493e-04\n",
      "Epoch 87/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0095 - accuracy: 6.8493e-04\n",
      "Epoch 88/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0098 - accuracy: 6.8493e-04\n",
      "Epoch 89/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n",
      "Epoch 90/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 6.8493e-04\n",
      "Epoch 91/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n",
      "Epoch 92/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 6.8493e-04\n",
      "Epoch 93/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 6.8493e-04\n",
      "Epoch 94/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n",
      "Epoch 95/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n",
      "Epoch 96/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 6.8493e-04\n",
      "Epoch 97/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 6.8493e-04\n",
      "Epoch 98/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n",
      "Epoch 99/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0090 - accuracy: 6.8493e-04\n",
      "Epoch 100/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0093 - accuracy: 6.8493e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7a0c9c67d150>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create and fit the LSTM network\n",
    "model_humid = Sequential()\n",
    "#Adding the first LSTM layer and some Dropout regularisation\n",
    "model_humid.add(LSTM(units = 100, return_sequences = True, input_shape = (trainX.shape[1], 1)))\n",
    "model_humid.add(Dropout(0.2))\n",
    "# Adding a second LSTM layer and some Dropout regularisation\n",
    "model_humid.add(LSTM(units = 100, return_sequences = True))\n",
    "model_humid.add(Dropout(0.2))\n",
    "# Adding a third LSTM layer and some Dropout regularisation\n",
    "model_humid.add(LSTM(units = 100, return_sequences = True))\n",
    "model_humid.add(Dropout(0.2))\n",
    "# Adding a fourth LSTM layer and some Dropout regularisation\n",
    "model_humid.add(LSTM(units = 50))\n",
    "model_humid.add(Dropout(0.2))\n",
    "# Adding the output layer\n",
    "model_humid.add(Dense(units = 1))\n",
    "\n",
    "# Compiling the RNN\n",
    "model_humid.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics=['accuracy'])\n",
    "\n",
    "# Fitting the RNN to the Training set\n",
    "model_humid.fit(trainX, trainY, epochs = 100, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:48:35.761730Z",
     "iopub.status.busy": "2024-07-02T15:48:35.761060Z",
     "iopub.status.idle": "2024-07-02T15:48:35.825040Z",
     "shell.execute_reply": "2024-07-02T15:48:35.823780Z",
     "shell.execute_reply.started": "2024-07-02T15:48:35.761661Z"
    }
   },
   "outputs": [],
   "source": [
    "# PREDICTION\n",
    "\n",
    "prediction = model_temp.predict(testX)\n",
    "prediction = ss.inverse_transform(prediction)\n",
    "humid_test = ss.inverse_transform(humid_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:48:35.827181Z",
     "iopub.status.busy": "2024-07-02T15:48:35.826815Z",
     "iopub.status.idle": "2024-07-02T15:48:36.092416Z",
     "shell.execute_reply": "2024-07-02T15:48:36.091427Z",
     "shell.execute_reply.started": "2024-07-02T15:48:35.827146Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(humid_test, color = 'black', label = 'Delhi Humidity')\n",
    "plt.plot(prediction, color = 'green', label = 'Predicted Delhi Humidity')\n",
    "plt.title('Delhi Humidity Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Humidity')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicting Delhi Wind Speed**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:48:36.098500Z",
     "iopub.status.busy": "2024-07-02T15:48:36.098127Z",
     "iopub.status.idle": "2024-07-02T15:48:36.104549Z",
     "shell.execute_reply": "2024-07-02T15:48:36.103111Z",
     "shell.execute_reply.started": "2024-07-02T15:48:36.098465Z"
    }
   },
   "outputs": [],
   "source": [
    "## Wind speed column \n",
    "wind_train = train.iloc[:,3:4]\n",
    "wind_test = test.iloc[:,3:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:48:36.107287Z",
     "iopub.status.busy": "2024-07-02T15:48:36.106790Z",
     "iopub.status.idle": "2024-07-02T15:48:36.127991Z",
     "shell.execute_reply": "2024-07-02T15:48:36.126910Z",
     "shell.execute_reply.started": "2024-07-02T15:48:36.107235Z"
    }
   },
   "outputs": [],
   "source": [
    "#Scaling the values between 0 to 1\n",
    "\n",
    "wind_train= ss.fit_transform(wind_train)\n",
    "wind_test= ss.fit_transform(wind_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:48:36.130135Z",
     "iopub.status.busy": "2024-07-02T15:48:36.129787Z",
     "iopub.status.idle": "2024-07-02T15:48:36.148762Z",
     "shell.execute_reply": "2024-07-02T15:48:36.147446Z",
     "shell.execute_reply.started": "2024-07-02T15:48:36.130102Z"
    }
   },
   "outputs": [],
   "source": [
    "look_back = 1\n",
    "trainX, trainY = create_dataset(wind_train, look_back)\n",
    "testX, testY = create_dataset(wind_test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:48:36.151447Z",
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     "shell.execute_reply": "2024-07-02T15:48:36.157554Z",
     "shell.execute_reply.started": "2024-07-02T15:48:36.151383Z"
    }
   },
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1],1))\n",
    "testX = np.reshape(testX, (testX.shape[0], testX.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2024-07-02T15:48:36.161088Z"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0181 - accuracy: 0.0164\n",
      "Epoch 2/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0112 - accuracy: 0.0164\n",
      "Epoch 3/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0102 - accuracy: 0.0164\n",
      "Epoch 4/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0098 - accuracy: 0.0164\n",
      "Epoch 5/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0097 - accuracy: 0.0164\n",
      "Epoch 6/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0098 - accuracy: 0.0164\n",
      "Epoch 7/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0095 - accuracy: 0.0164\n",
      "Epoch 8/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0097 - accuracy: 0.0164\n",
      "Epoch 9/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0096 - accuracy: 0.0164\n",
      "Epoch 10/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0098 - accuracy: 0.0164\n",
      "Epoch 11/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0096 - accuracy: 0.0164\n",
      "Epoch 12/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 0.0164\n",
      "Epoch 13/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0096 - accuracy: 0.0164\n",
      "Epoch 14/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0095 - accuracy: 0.0164\n",
      "Epoch 15/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0095 - accuracy: 0.0164\n",
      "Epoch 16/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 0.0164\n",
      "Epoch 17/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 0.0164\n",
      "Epoch 18/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 19/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 20/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 21/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 22/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 23/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 24/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 0.0164\n",
      "Epoch 25/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 26/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 27/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 28/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 29/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 30/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 31/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 32/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 33/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 34/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 35/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 36/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 37/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 38/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 39/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 0.0164\n",
      "Epoch 40/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0091 - accuracy: 0.0164\n",
      "Epoch 41/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 42/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 43/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 44/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 45/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 46/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0094 - accuracy: 0.0164\n",
      "Epoch 47/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 48/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 49/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 50/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 51/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 52/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 53/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 54/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 55/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 56/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 57/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 58/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 59/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 60/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0091 - accuracy: 0.0164\n",
      "Epoch 61/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 62/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 63/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 64/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0091 - accuracy: 0.0164\n",
      "Epoch 65/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 66/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 67/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 68/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 69/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 70/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 71/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 72/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0091 - accuracy: 0.0164\n",
      "Epoch 73/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 74/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 75/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 76/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 77/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 78/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 79/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0091 - accuracy: 0.0164\n",
      "Epoch 80/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 81/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 82/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 83/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 84/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 85/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 86/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 87/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 88/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 89/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 90/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 91/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0093 - accuracy: 0.0164\n",
      "Epoch 92/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 93/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 94/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0091 - accuracy: 0.0164\n",
      "Epoch 95/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0091 - accuracy: 0.0164\n",
      "Epoch 96/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 97/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0091 - accuracy: 0.0164\n",
      "Epoch 98/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 99/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0092 - accuracy: 0.0164\n",
      "Epoch 100/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0091 - accuracy: 0.0164\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7a0c6d471650>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create and fit the LSTM network\n",
    "model_wind = Sequential()\n",
    "#Adding the first LSTM layer and some Dropout regularisation\n",
    "model_wind.add(LSTM(units = 100, return_sequences = True, input_shape = (trainX.shape[1], 1)))\n",
    "model_wind.add(Dropout(0.2))\n",
    "# Adding a second LSTM layer and some Dropout regularisation\n",
    "model_wind.add(LSTM(units = 100, return_sequences = True))\n",
    "model_wind.add(Dropout(0.2))\n",
    "# Adding a third LSTM layer and some Dropout regularisation\n",
    "model_wind.add(LSTM(units = 100, return_sequences = True))\n",
    "model_wind.add(Dropout(0.2))\n",
    "# Adding a fourth LSTM layer and some Dropout regularisation\n",
    "model_wind.add(LSTM(units = 50))\n",
    "model_wind.add(Dropout(0.2))\n",
    "# Adding the output layer\n",
    "model_wind.add(Dense(units = 1))\n",
    "\n",
    "# Compiling the RNN\n",
    "model_wind.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics=['accuracy'])\n",
    "\n",
    "# Fitting the RNN to the Training set\n",
    "model_wind.fit(trainX, trainY, epochs = 100, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:49:22.363533Z",
     "iopub.status.busy": "2024-07-02T15:49:22.363039Z",
     "iopub.status.idle": "2024-07-02T15:49:22.426854Z",
     "shell.execute_reply": "2024-07-02T15:49:22.425475Z",
     "shell.execute_reply.started": "2024-07-02T15:49:22.363479Z"
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   },
   "outputs": [],
   "source": [
    "# PREDICTION\n",
    "\n",
    "prediction = model_temp.predict(testX)\n",
    "prediction = ss.inverse_transform(prediction)\n",
    "wind_test = ss.inverse_transform(wind_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:49:22.429186Z",
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     "shell.execute_reply.started": "2024-07-02T15:49:22.429133Z"
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   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(wind_test, color = 'black', label = 'Delhi Wind Speed')\n",
    "plt.plot(prediction, color = 'green', label = 'Predicted Delhi Wind Speed')\n",
    "plt.title('Delhi Wind Speed Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Wind Speed')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicting Mean Pressure of Delhi**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:49:22.707399Z",
     "iopub.status.busy": "2024-07-02T15:49:22.706910Z",
     "iopub.status.idle": "2024-07-02T15:49:22.714680Z",
     "shell.execute_reply": "2024-07-02T15:49:22.713418Z",
     "shell.execute_reply.started": "2024-07-02T15:49:22.707350Z"
    }
   },
   "outputs": [],
   "source": [
    "## Mean Pressure column \n",
    "pressure_train = train.iloc[:,4:]\n",
    "pressure_test = test.iloc[:,4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:49:22.716971Z",
     "iopub.status.busy": "2024-07-02T15:49:22.716473Z",
     "iopub.status.idle": "2024-07-02T15:49:22.738717Z",
     "shell.execute_reply": "2024-07-02T15:49:22.737504Z",
     "shell.execute_reply.started": "2024-07-02T15:49:22.716920Z"
    }
   },
   "outputs": [],
   "source": [
    "#Scaling the values between 0 to 1\n",
    "\n",
    "pressure_train= ss.fit_transform(pressure_train)\n",
    "pressure_test= ss.fit_transform(pressure_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:49:22.740572Z",
     "iopub.status.busy": "2024-07-02T15:49:22.740192Z",
     "iopub.status.idle": "2024-07-02T15:49:22.756862Z",
     "shell.execute_reply": "2024-07-02T15:49:22.755838Z",
     "shell.execute_reply.started": "2024-07-02T15:49:22.740528Z"
    }
   },
   "outputs": [],
   "source": [
    "trainX, trainY = create_dataset(pressure_train, look_back)\n",
    "testX, testY = create_dataset(pressure_test, look_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:49:22.759400Z",
     "iopub.status.busy": "2024-07-02T15:49:22.758897Z",
     "iopub.status.idle": "2024-07-02T15:49:22.766764Z",
     "shell.execute_reply": "2024-07-02T15:49:22.765527Z",
     "shell.execute_reply.started": "2024-07-02T15:49:22.759349Z"
    }
   },
   "outputs": [],
   "source": [
    "# reshape input to be [samples, time steps, features]\n",
    "trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1],1))\n",
    "testX = np.reshape(testX, (testX.shape[0], testX.shape[1],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-02T15:49:22.768980Z",
     "iopub.status.busy": "2024-07-02T15:49:22.768481Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 0.0043 - accuracy: 6.8493e-04\n",
      "Epoch 2/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.7094e-04 - accuracy: 6.8493e-04\n",
      "Epoch 3/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.4274e-04 - accuracy: 6.8493e-04\n",
      "Epoch 4/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.3472e-04 - accuracy: 6.8493e-04\n",
      "Epoch 5/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.4043e-04 - accuracy: 6.8493e-04\n",
      "Epoch 6/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.3264e-04 - accuracy: 6.8493e-04\n",
      "Epoch 7/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.0679e-04 - accuracy: 6.8493e-04\n",
      "Epoch 8/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.2887e-04 - accuracy: 6.8493e-04\n",
      "Epoch 9/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.3683e-04 - accuracy: 6.8493e-04\n",
      "Epoch 10/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.1718e-04 - accuracy: 6.8493e-04\n",
      "Epoch 11/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.9906e-04 - accuracy: 6.8493e-04\n",
      "Epoch 12/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.1898e-04 - accuracy: 6.8493e-04\n",
      "Epoch 13/100\n",
      "46/46 [==============================] - 0s 10ms/step - loss: 5.9883e-04 - accuracy: 6.8493e-04\n",
      "Epoch 14/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 6.0297e-04 - accuracy: 6.8493e-04\n",
      "Epoch 15/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.1500e-04 - accuracy: 6.8493e-04\n",
      "Epoch 16/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.9797e-04 - accuracy: 6.8493e-04\n",
      "Epoch 17/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.8904e-04 - accuracy: 6.8493e-04\n",
      "Epoch 18/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 5.9807e-04 - accuracy: 6.8493e-04\n",
      "Epoch 19/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.0280e-04 - accuracy: 6.8493e-04\n",
      "Epoch 20/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.9074e-04 - accuracy: 6.8493e-04\n",
      "Epoch 21/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.9942e-04 - accuracy: 6.8493e-04\n",
      "Epoch 22/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.0658e-04 - accuracy: 6.8493e-04\n",
      "Epoch 23/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.8366e-04 - accuracy: 6.8493e-04\n",
      "Epoch 24/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 6.0136e-04 - accuracy: 6.8493e-04\n",
      "Epoch 25/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.8808e-04 - accuracy: 6.8493e-04\n",
      "Epoch 26/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.8426e-04 - accuracy: 6.8493e-04\n",
      "Epoch 27/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.8392e-04 - accuracy: 6.8493e-04\n",
      "Epoch 28/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.8471e-04 - accuracy: 6.8493e-04\n",
      "Epoch 29/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.8694e-04 - accuracy: 6.8493e-04\n",
      "Epoch 30/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 5.8134e-04 - accuracy: 6.8493e-04\n",
      "Epoch 31/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.8877e-04 - accuracy: 6.8493e-04\n",
      "Epoch 32/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6743e-04 - accuracy: 6.8493e-04\n",
      "Epoch 33/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.7901e-04 - accuracy: 6.8493e-04\n",
      "Epoch 34/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6577e-04 - accuracy: 6.8493e-04\n",
      "Epoch 35/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.7570e-04 - accuracy: 6.8493e-04\n",
      "Epoch 36/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5876e-04 - accuracy: 6.8493e-04\n",
      "Epoch 37/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.7038e-04 - accuracy: 6.8493e-04\n",
      "Epoch 38/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.8008e-04 - accuracy: 6.8493e-04\n",
      "Epoch 39/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 5.7134e-04 - accuracy: 6.8493e-04\n",
      "Epoch 40/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 5.6605e-04 - accuracy: 6.8493e-04\n",
      "Epoch 41/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 5.6552e-04 - accuracy: 6.8493e-04\n",
      "Epoch 42/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 5.6083e-04 - accuracy: 6.8493e-04\n",
      "Epoch 43/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 5.7389e-04 - accuracy: 6.8493e-04\n",
      "Epoch 44/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6490e-04 - accuracy: 6.8493e-04\n",
      "Epoch 45/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6722e-04 - accuracy: 6.8493e-04\n",
      "Epoch 46/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.7069e-04 - accuracy: 6.8493e-04\n",
      "Epoch 47/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.7439e-04 - accuracy: 6.8493e-04\n",
      "Epoch 48/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6840e-04 - accuracy: 6.8493e-04\n",
      "Epoch 49/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5942e-04 - accuracy: 6.8493e-04\n",
      "Epoch 50/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5909e-04 - accuracy: 6.8493e-04\n",
      "Epoch 51/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6998e-04 - accuracy: 6.8493e-04\n",
      "Epoch 52/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6735e-04 - accuracy: 6.8493e-04\n",
      "Epoch 53/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6283e-04 - accuracy: 6.8493e-04\n",
      "Epoch 54/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6775e-04 - accuracy: 6.8493e-04\n",
      "Epoch 55/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6290e-04 - accuracy: 6.8493e-04\n",
      "Epoch 56/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5790e-04 - accuracy: 6.8493e-04\n",
      "Epoch 57/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6242e-04 - accuracy: 6.8493e-04\n",
      "Epoch 58/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6006e-04 - accuracy: 6.8493e-04\n",
      "Epoch 59/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5737e-04 - accuracy: 6.8493e-04\n",
      "Epoch 60/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5896e-04 - accuracy: 6.8493e-04\n",
      "Epoch 61/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5836e-04 - accuracy: 6.8493e-04\n",
      "Epoch 62/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6104e-04 - accuracy: 6.8493e-04\n",
      "Epoch 63/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6797e-04 - accuracy: 6.8493e-04\n",
      "Epoch 64/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6000e-04 - accuracy: 6.8493e-04\n",
      "Epoch 65/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 5.6366e-04 - accuracy: 6.8493e-04\n",
      "Epoch 66/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6555e-04 - accuracy: 6.8493e-04\n",
      "Epoch 67/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6336e-04 - accuracy: 6.8493e-04\n",
      "Epoch 68/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6028e-04 - accuracy: 6.8493e-04\n",
      "Epoch 69/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5950e-04 - accuracy: 6.8493e-04\n",
      "Epoch 70/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6217e-04 - accuracy: 6.8493e-04\n",
      "Epoch 71/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5897e-04 - accuracy: 6.8493e-04\n",
      "Epoch 72/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6101e-04 - accuracy: 6.8493e-04\n",
      "Epoch 73/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5622e-04 - accuracy: 6.8493e-04\n",
      "Epoch 74/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5918e-04 - accuracy: 6.8493e-04\n",
      "Epoch 75/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5827e-04 - accuracy: 6.8493e-04\n",
      "Epoch 76/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5347e-04 - accuracy: 6.8493e-04\n",
      "Epoch 77/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5656e-04 - accuracy: 6.8493e-04\n",
      "Epoch 78/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5739e-04 - accuracy: 6.8493e-04\n",
      "Epoch 79/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6409e-04 - accuracy: 6.8493e-04\n",
      "Epoch 80/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6002e-04 - accuracy: 6.8493e-04\n",
      "Epoch 81/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5865e-04 - accuracy: 6.8493e-04\n",
      "Epoch 82/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5775e-04 - accuracy: 6.8493e-04\n",
      "Epoch 83/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5461e-04 - accuracy: 6.8493e-04\n",
      "Epoch 84/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.7058e-04 - accuracy: 6.8493e-04\n",
      "Epoch 85/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5453e-04 - accuracy: 6.8493e-04\n",
      "Epoch 86/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5855e-04 - accuracy: 6.8493e-04\n",
      "Epoch 87/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5675e-04 - accuracy: 6.8493e-04\n",
      "Epoch 88/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.6109e-04 - accuracy: 6.8493e-04\n",
      "Epoch 89/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5752e-04 - accuracy: 6.8493e-04\n",
      "Epoch 90/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5538e-04 - accuracy: 6.8493e-04\n",
      "Epoch 91/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 5.5780e-04 - accuracy: 6.8493e-04\n",
      "Epoch 92/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5643e-04 - accuracy: 6.8493e-04\n",
      "Epoch 93/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5724e-04 - accuracy: 6.8493e-04\n",
      "Epoch 94/100\n",
      "46/46 [==============================] - 0s 9ms/step - loss: 5.5809e-04 - accuracy: 6.8493e-04\n",
      "Epoch 95/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5318e-04 - accuracy: 6.8493e-04\n",
      "Epoch 96/100\n",
      "46/46 [==============================] - 0s 8ms/step - loss: 5.5755e-04 - accuracy: 6.8493e-04\n",
      "Epoch 97/100\n",
      "13/46 [=======>......................] - ETA: 0s - loss: 8.4620e-05 - accuracy: 0.0000e+00"
     ]
    }
   ],
   "source": [
    "# create and fit the LSTM network\n",
    "model_pressure = Sequential()\n",
    "#Adding the first LSTM layer and some Dropout regularisation\n",
    "model_pressure.add(LSTM(units = 100, return_sequences = True, input_shape = (trainX.shape[1], 1)))\n",
    "model_pressure.add(Dropout(0.2))\n",
    "# Adding a second LSTM layer and some Dropout regularisation\n",
    "model_pressure.add(LSTM(units = 100, return_sequences = True))\n",
    "model_pressure.add(Dropout(0.2))\n",
    "# Adding a third LSTM layer and some Dropout regularisation\n",
    "model_pressure.add(LSTM(units = 100, return_sequences = True))\n",
    "model_pressure.add(Dropout(0.2))\n",
    "# Adding a fourth LSTM layer and some Dropout regularisation\n",
    "model_pressure.add(LSTM(units = 50))\n",
    "model_pressure.add(Dropout(0.2))\n",
    "# Adding the output layer\n",
    "model_pressure.add(Dense(units = 1))\n",
    "\n",
    "# Compiling the RNN\n",
    "model_pressure.compile(optimizer = 'adam', loss = 'mean_squared_error', metrics=['accuracy'])\n",
    "\n",
    "# Fitting the RNN to the Training set\n",
    "model_pressure.fit(trainX, trainY, epochs = 100, batch_size = 32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PREDICTION\n",
    "\n",
    "prediction = model_temp.predict(testX)\n",
    "prediction = ss.inverse_transform(prediction)\n",
    "pressure_test = ss.inverse_transform(pressure_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.plot(pressure_test, color = 'black', label = 'Delhi Mean Pressure')\n",
    "plt.plot(prediction, color = 'green', label = 'Predicted Delhi Mean Pressure')\n",
    "plt.title('Delhi Mean Pressure Prediction')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Mean Pressure')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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